tile.cpp

#ifdef __APPLE__
#define _DARWIN_UNLIMITED_STREAMS
#endif

#include <iostream>
#include <fstream>
#include <string>
#include <stack>
#include <vector>
#include <map>
#include <unordered_map>
#include <set>
#include <memory>
#include <algorithm>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <limits.h>
#include <zlib.h>
#include <sys/stat.h>
#include <sys/types.h>
#include <sys/mman.h>
#include <cmath>
#include <sqlite3.h>
#include <pthread.h>
#include <errno.h>
#include <time.h>
#include <fcntl.h>
#include <zlib.h>
#include <sys/wait.h>
#include "mvt.hpp"
#include "mbtiles.hpp"
#include "dirtiles.hpp"
#include "geometry.hpp"
#include "tile.hpp"
#include "pool.hpp"
#include "projection.hpp"
#include "serial.hpp"
#include "options.hpp"
#include "main.hpp"
#include "write_json.hpp"
#include "milo/dtoa_milo.h"
#include "evaluator.hpp"
#include "errors.hpp"
#include "compression.hpp"
#include "protozero/varint.hpp"
#include "attribute.hpp"
#include "thread.hpp"
#include "shared_borders.hpp"

extern "C" {
#include "jsonpull/jsonpull.h"
}

#include "plugin.hpp"

#define CMD_BITS 3

// Offset coordinates to keep them positive
#define COORD_OFFSET (4LL << 32)
#define SHIFT_RIGHT(a) ((long long) std::round((double) (a) / (1LL << geometry_scale)))

pthread_mutex_t db_lock = PTHREAD_MUTEX_INITIALIZER;
pthread_mutex_t var_lock = PTHREAD_MUTEX_INITIALIZER;
pthread_mutex_t task_lock = PTHREAD_MUTEX_INITIALIZER;

// convert serial feature geometry (drawvec) to output tile geometry (mvt_geometry)
static std::vector<mvt_geometry> to_feature(drawvec const &geom) {
	std::vector<mvt_geometry> out;

	for (size_t i = 0; i < geom.size(); i++) {
		out.emplace_back(geom[i].op, geom[i].x, geom[i].y);
	}

	return out;
}

// does this geometry have any non-zero-length linetos?
static bool draws_something(drawvec const &geom) {
	for (size_t i = 1; i < geom.size(); i++) {
		if (geom[i].op == VT_LINETO && (geom[i].x != geom[i - 1].x || geom[i].y != geom[i - 1].y)) {
			return true;
		}
	}

	return false;
}

// comparator for --preserve-input-order, to reorder features back to their original input sequence
static struct preservecmp {
	bool operator()(const std::vector<std::shared_ptr<serial_feature>> &a, const std::vector<std::shared_ptr<serial_feature>> &b) {
		return operator()(a[0], b[0]);
	}

	bool operator()(const serial_feature &a, const serial_feature &b) {
		return a.seq < b.seq;
	}

	bool operator()(const std::shared_ptr<serial_feature> &a, const std::shared_ptr<serial_feature> &b) {
		return a->seq < b->seq;
	}
} preservecmp;

static int metacmp(const serial_feature &one, const serial_feature &two);

// comparator for --coalesce and --reorder:
// two features can be coalesced if they have
// * the same type
// * the same id, if any
// * the same attributes, according to metacmp
// * the same full_keys and full_values attributes
static int coalcmp(const void *v1, const void *v2) {
	const serial_feature *c1 = (const serial_feature *) v1;
	const serial_feature *c2 = (const serial_feature *) v2;

	int cmp = c1->t - c2->t;
	if (cmp != 0) {
		return cmp;
	}

	if (c1->has_id != c2->has_id) {
		return (int) c1->has_id - (int) c2->has_id;
	}

	if (c1->has_id && c2->has_id) {
		if (c1->id < c2->id) {
			return -1;
		}
		if (c1->id > c2->id) {
			return 1;
		}
	}

	cmp = metacmp(*c1, *c2);
	if (cmp != 0) {
		return cmp;
	}

	if (c1->full_keys.size() < c2->full_keys.size()) {
		return -1;
	} else if (c1->full_keys.size() > c2->full_keys.size()) {
		return 1;
	}

	for (size_t i = 0; i < c1->full_keys.size(); i++) {
		if (*c1->full_keys[i] < *c2->full_keys[i]) {
			return -1;
		} else if (*c1->full_keys[i] > *c2->full_keys[i]) {
			return 1;
		}

		if (c1->full_values[i].type < c2->full_values[i].type) {
			return -1;
		} else if (c1->full_values[i].type > c2->full_values[i].type) {
			return 1;
		}

		if (c1->full_values[i].s < c2->full_values[i].s) {
			return -1;
		} else if (c1->full_values[i].s > c2->full_values[i].s) {
			return 1;
		}
	}

	return 0;
}

// comparator for --reorder:
// features are ordered first by their attributes (according to coalcmp above)
// and then, if they are identical from that perspective, by their index (centroid)
// and geometry
struct coalindexcmp_comparator {
	int coalindexcmp(const serial_feature *c1, const serial_feature *c2) const {
		int cmp = coalcmp((const void *) c1, (const void *) c2);

		if (cmp == 0) {
			if (c1->index < c2->index) {
				return -1;
			} else if (c1->index > c2->index) {
				return 1;
			}

			if (c1->geometry < c2->geometry) {
				return -1;
			} else if (c1->geometry > c2->geometry) {
				return 1;
			}
		}

		return cmp;
	}

	bool operator()(const std::shared_ptr<serial_feature> &a, const std::shared_ptr<serial_feature> &o) const {
		int cmp = coalindexcmp(&*a, &*o);
		if (cmp < 0) {
			return true;
		} else {
			return false;
		}
	}
};

static unsigned long long calculate_drop_sequence(serial_feature const &sf);

struct drop_sequence_cmp {
	bool operator()(const std::shared_ptr<serial_feature> &a, const std::shared_ptr<serial_feature> &b) {
		unsigned long long a_seq = calculate_drop_sequence(*a);
		unsigned long long b_seq = calculate_drop_sequence(*b);

		// sorts backwards, to put the features that would be dropped last, first here
		if (a_seq > b_seq) {
			return true;
		} else {
			return false;
		}
	}
};

// retrieve an attribute key or value from the string pool and return it as mvt_value
static mvt_value retrieve_string(long long off, const char *stringpool, std::shared_ptr<std::string> const &tile_stringpool) {
	int type = stringpool[off];
	const char *s = stringpool + off + 1;

	return stringified_to_mvt_value(type, s, tile_stringpool);
}

// retrieve an attribute key from the string pool and return it as std::string
static std::string retrieve_std_string(long long off, const char *stringpool) {
	return std::string(stringpool + off + 1);
}

// retrieve the keys and values of a feature from the string pool
// and tag them onto an mvt_feature and mvt_layer
static void decode_meta(serial_feature const &sf, mvt_layer &layer, mvt_feature &feature) {
	size_t i;
	for (i = 0; i < sf.keys.size(); i++) {
		std::string key = retrieve_std_string(sf.keys[i], sf.stringpool);
		mvt_value value = retrieve_string(sf.values[i], sf.stringpool, sf.tile_stringpool);

		layer.tag(feature, key, value);
	}
}

// comparator used to check whether two features have identical keys and values,
// as determined by retrieving them from the string pool. The order of keys,
// not just the content of their values, must also be identical for them to compare equal.
static int metacmp(const serial_feature &one, const serial_feature &two) {
	if (one.keys.size() < two.keys.size()) {
		return -1;
	} else if (one.keys.size() > two.keys.size()) {
		return 1;
	}

	size_t i;
	for (i = 0; i < one.keys.size() && i < two.keys.size(); i++) {
		const char *key1 = one.stringpool + one.keys[i] + 1;
		const char *key2 = two.stringpool + two.keys[i] + 1;

		int cmp = strcmp(key1, key2);
		if (cmp != 0) {
			return cmp;
		}

		long long off1 = one.values[i];
		int type1 = one.stringpool[off1];
		const char *s1 = one.stringpool + off1 + 1;

		long long off2 = two.values[i];
		int type2 = two.stringpool[off2];
		const char *s2 = two.stringpool + off2 + 1;

		if (type1 != type2) {
			return type1 - type2;
		}
		cmp = strcmp(s1, s2);
		if (cmp != 0) {
			return cmp;
		}
	}

	return 0;
}

// Retrieve the value of an attribute or pseudo-attribute (ORDER_BY_SIZE) for --order purposes.
static mvt_value find_attribute_value(const serial_feature *c1, std::string const &key) {
	if (key == ORDER_BY_SIZE) {
		mvt_value v;
		v.type = mvt_double;
		v.numeric_value.double_value = c1->extent;
		return v;
	}

	const std::vector<long long> &keys1 = c1->keys;
	const std::vector<long long> &values1 = c1->values;
	const char *stringpool1 = c1->stringpool;

	for (size_t i = 0; i < keys1.size(); i++) {
		const char *key1 = stringpool1 + keys1[i] + 1;
		if (strcmp(key1, key.c_str()) == 0) {
			return retrieve_string(values1[i], stringpool1, c1->tile_stringpool);
		}
	}

	for (size_t i = 0; i < c1->full_keys.size(); i++) {
		if (*c1->full_keys[i] == key) {
			return stringified_to_mvt_value(c1->full_values[i].type, c1->full_values[i].s.c_str(), c1->tile_stringpool);
		}
	}

	mvt_value v;
	v.type = mvt_null;
	v.numeric_value.null_value = 0;
	return v;
}

// Ensure that two mvt_values can be compared numerically by converting other numeric types to mvt_double
static mvt_value coerce_double(mvt_value v) {
	if (v.type == mvt_int) {
		v.type = mvt_double;
		v.numeric_value.double_value = v.numeric_value.int_value;
	} else if (v.type == mvt_uint) {
		v.type = mvt_double;
		v.numeric_value.double_value = v.numeric_value.uint_value;
	} else if (v.type == mvt_sint) {
		v.type = mvt_double;
		v.numeric_value.double_value = v.numeric_value.sint_value;
	} else if (v.type == mvt_float) {
		v.type = mvt_double;
		v.numeric_value.double_value = v.numeric_value.float_value;
	}

	return v;
}

// comparator for ordering features for --order: for each sort key that the user has specified,
// compare features numerically according to that sort key until the keys are exhausted.
// If there is a tie, the feature with the earlier index (centroid) comes first.
struct ordercmp {
	bool operator()(const std::vector<std::shared_ptr<serial_feature>> &a, const std::vector<std::shared_ptr<serial_feature>> &b) {
		return operator()(a[0], b[0]);
	}

	bool operator()(const std::shared_ptr<serial_feature> &a, const std::shared_ptr<serial_feature> &b) {
		for (size_t i = 0; i < order_by.size(); i++) {
			mvt_value v1 = coerce_double(find_attribute_value(&*a, order_by[i].name));
			mvt_value v2 = coerce_double(find_attribute_value(&*b, order_by[i].name));

			if (order_by[i].descending) {
				if (v2 < v1) {
					return true;
				} else if (v1 < v2) {
					return false;
				}  // else they are equal, so continue to the next attribute
			} else {
				if (v1 < v2) {
					return true;
				} else if (v2 < v1) {
					return false;
				}  // else they are equal, so continue to the next attribute
			}
		}

		if (a->index < b->index) {
			return true;
		}

		return false;  // greater than or equal
	}
};

// For --retain-points-multiplier: Go through a list of features and return a list of clusters of features,
// creating a new cluster whenever the tippecanoe:retain_points_multiplier_first attribute is seen.
static std::vector<std::vector<std::shared_ptr<serial_feature>>> assemble_multiplier_clusters(std::vector<std::shared_ptr<serial_feature>> const &features) {
	std::vector<std::vector<std::shared_ptr<serial_feature>>> clusters;

	if (retain_points_multiplier == 1) {
		for (auto const &feature : features) {
			std::vector<std::shared_ptr<serial_feature>> cluster;
			cluster.push_back(std::move(feature));
			clusters.push_back(std::move(cluster));
		}
	} else {
		for (auto const &feature : features) {
			bool is_cluster_start = false;

			for (size_t i = 0; i < feature->full_keys.size(); i++) {
				if (*feature->full_keys[i] == "tippecanoe:retain_points_multiplier_first") {
					is_cluster_start = true;
					break;
				}
			}

			if (is_cluster_start || clusters.size() == 0) {
				clusters.emplace_back();
			}

			clusters.back().push_back(std::move(feature));
		}
	}

	return clusters;
}

// For --retain-points-multiplier: Flatten a list of clusters of features back into a list of features,
// moving the "tippecanoe:retain_points_multiplier_first" attribute onto the first feature of each cluster
// if it is not already there.
static std::vector<std::shared_ptr<serial_feature>> disassemble_multiplier_clusters(std::vector<std::vector<std::shared_ptr<serial_feature>>> &clusters) {
	std::vector<std::shared_ptr<serial_feature>> out;

	for (auto &cluster : clusters) {
		// fix up the attributes so the first feature of the multiplier cluster
		// gets the marker attribute
		for (size_t i = 0; i < cluster.size(); i++) {
			for (size_t j = 0; j < cluster[i]->full_keys.size(); j++) {
				if (*cluster[i]->full_keys[j] == "tippecanoe:retain_points_multiplier_first") {
					cluster[0]->full_keys.push_back(std::move(cluster[i]->full_keys[j]));
					cluster[0]->full_values.push_back(std::move(cluster[i]->full_values[j]));

					cluster[i]->full_keys.erase(cluster[i]->full_keys.begin() + j);
					cluster[i]->full_values.erase(cluster[i]->full_values.begin() + j);

					i = cluster.size();  // break outer
					break;
				}
			}
		}

		// sort the other features by their drop sequence, for consistency across zoom levels
		if (cluster.size() > 1) {
			std::stable_sort(cluster.begin() + 1, cluster.end(), drop_sequence_cmp());
		}

		for (auto const &feature : cluster) {
			out.push_back(std::move(feature));
		}
	}

	return out;
}

// Write out copies of a feature into the temporary files for the next zoom level
static void rewrite(serial_feature const &osf, int z, int nextzoom, int maxzoom, unsigned tx, unsigned ty, int buffer, std::atomic<bool> within[], std::atomic<long long> *geompos, long long start_geompos[], compressor *geomfile[], const char *fname, int child_shards, int max_zoom_increment, int segment, unsigned *initial_x, unsigned *initial_y) {
	if (osf.geometry.size() > 0 && (nextzoom <= maxzoom || additional[A_EXTEND_ZOOMS] || extend_zooms_max > 0)) {
		int xo, yo;
		int span = 1 << (nextzoom - z);

		// Get the feature bounding box in pixel (256) coordinates at the child zoom
		// in order to calculate which sub-tiles it can touch including the buffer.
		long long bbox2[4];
		int k;
		for (k = 0; k < 4; k++) {
			// Division instead of right-shift because coordinates can be negative
			bbox2[k] = osf.bbox[k] / (1 << (32 - nextzoom - 8));
		}
		// Decrement the top and left edges so that any features that are
		// touching the edge can potentially be included in the adjacent tiles too.
		bbox2[0] -= buffer + 1;
		bbox2[1] -= buffer + 1;
		bbox2[2] += buffer;
		bbox2[3] += buffer;

		for (k = 0; k < 4; k++) {
			if (bbox2[k] < 0) {
				bbox2[k] = 0;
			}
			if (bbox2[k] >= 256 * span) {
				bbox2[k] = 256 * (span - 1);
			}

			bbox2[k] /= 256;
		}

		// Offset from tile coordinates back to world coordinates
		unsigned sx = 0, sy = 0;
		if (z != 0) {
			sx = tx << (32 - z);
			sy = ty << (32 - z);
		}

		drawvec geom2;
		for (auto const &g : osf.geometry) {
			geom2.emplace_back(g.op, SHIFT_RIGHT(g.x + sx), SHIFT_RIGHT(g.y + sy));
		}

		for (xo = bbox2[0]; xo <= bbox2[2]; xo++) {
			for (yo = bbox2[1]; yo <= bbox2[3]; yo++) {
				unsigned jx = tx * span + xo;
				unsigned jy = ty * span + yo;

				// j is the shard that the child tile's data is being written to.
				//
				// Be careful: We can't jump more zoom levels than max_zoom_increment
				// because that could break the constraint that each of the children
				// of the current tile must have its own shard, because the data for
				// the child tile must be contiguous within the shard.
				//
				// But it's OK to spread children across all the shards, not just
				// the four that would normally result from splitting one tile,
				// because it will go through all the shards when it does the
				// next zoom.
				//
				// If child_shards is a power of 2 but not a power of 4, this will
				// shard X more widely than Y. XXX Is there a better way to do this
				// without causing collisions?

				int j = ((jx << max_zoom_increment) |
					 ((jy & ((1 << max_zoom_increment) - 1)))) &
					(child_shards - 1);

				{
					if (!within[j]) {
						within[j] = true;
						start_geompos[j] = geompos[j];	// no competition between threads

						long long estimated_complexity = 0;  // placeholder, to be filled in later
						fwrite_check(&estimated_complexity, sizeof(estimated_complexity), 1, geomfile[j]->fp, &geompos[j], fname);
						serialize_int(geomfile[j]->fp, nextzoom, &geompos[j], fname);
						serialize_uint(geomfile[j]->fp, tx * span + xo, &geompos[j], fname);
						serialize_uint(geomfile[j]->fp, ty * span + yo, &geompos[j], fname);
						geomfile[j]->begin();
					}

					serial_feature sf = osf;
					sf.geometry = geom2;

					std::string feature = serialize_feature(&sf, SHIFT_RIGHT(initial_x[segment]), SHIFT_RIGHT(initial_y[segment]));
					geomfile[j]->serialize_long_long(feature.size(), &geompos[j], fname);
					geomfile[j]->fwrite_check(feature.c_str(), sizeof(char), feature.size(), &geompos[j], fname);
				}
			}
		}
	}
}

// This is the parameter block passed to each simplification worker thread
struct simplification_worker_arg {
	std::vector<std::shared_ptr<serial_feature>> *features = NULL;
	int task = 0;
	int tasks = 0;

	drawvec *shared_nodes;
	node *shared_nodes_map;
	size_t nodepos;
	std::string const *shared_nodes_bloom;
};

// If a polygon has collapsed away to nothing during polygon cleaning,
// this is the function that tries to replace it with a rectangular placeholder
// so that the area of the feature is still somehow represented
static drawvec revive_polygon(drawvec &geom, double area, int z, int detail) {
	// From area in world coordinates to area in tile coordinates
	long long divisor = 1LL << (32 - detail - z);
	area /= divisor * divisor;

	if (area == 0) {
		return drawvec();
	}

	int height = ceil(sqrt(area));
	int width = round(area / height);
	if (width == 0) {
		width = 1;
	}

	long long sx = 0, sy = 0, n = 0;
	for (size_t i = 0; i < geom.size(); i++) {
		if (geom[i].op == VT_MOVETO || geom[i].op == VT_LINETO) {
			sx += geom[i].x;
			sy += geom[i].y;
			n++;
		}
	}

	if (n > 0) {
		sx /= n;
		sy /= n;

		drawvec out;
		out.emplace_back(VT_MOVETO, sx - (width / 2), sy - (height / 2));
		out.emplace_back(VT_LINETO, sx - (width / 2) + width, sy - (height / 2));
		out.emplace_back(VT_LINETO, sx - (width / 2) + width, sy - (height / 2) + height);
		out.emplace_back(VT_LINETO, sx - (width / 2), sy - (height / 2) + height);
		out.emplace_back(VT_LINETO, sx - (width / 2), sy - (height / 2));

		return out;
	} else {
		return drawvec();
	}
}

// This simplifies the geometry of one feature. It is generally called from the feature_simplification_worker
// but is broken out here so that it can be called from earlier in write_tile if coalesced geometries build up
// too much in memory.
static double simplify_feature(serial_feature *p, drawvec const &shared_nodes, node *shared_nodes_map, size_t nodepos, std::string const &shared_nodes_bloom) {
	drawvec geom = p->geometry;
	signed char t = p->t;
	int z = p->z;
	int line_detail = p->line_detail;
	int maxzoom = p->maxzoom;

	if (additional[A_GRID_LOW_ZOOMS] && z < maxzoom) {
		geom = stairstep(geom, z, line_detail);
	}

	double area = 0;
	if (t == VT_POLYGON) {
		area = get_mp_area(geom);
	}

	if ((t == VT_LINE || t == VT_POLYGON) && !(prevent[P_SIMPLIFY] || (z == maxzoom && prevent[P_SIMPLIFY_LOW]) || (z < maxzoom && additional[A_GRID_LOW_ZOOMS]))) {
		// Now I finally remember why it doesn't simplify if the feature was reduced:
		// because it makes square placeholders look like weird triangular placeholders.
		// Only matters if simplification is set higher than the tiny polygon size.
		// Tiny polygons that are part of a tiny multipolygon will still get simplified.
		if (!p->reduced) {
			// These aren't necessarily actually no-ops until we scale down.
			// Don't do it if we are trying to preserve intersections, because
			// it might wipe out the intersection and spoil the matching even though
			// it would leave something else within the same tile pixel.
			if (t == VT_LINE && !prevent[P_SIMPLIFY_SHARED_NODES]) {
				// continues to deduplicate to line_detail even if we have extra detail
				geom = remove_noop(geom, t, 32 - z - line_detail);
			}

			bool already_marked = false;
			if (additional[A_DETECT_SHARED_BORDERS] && t == VT_POLYGON) {
				already_marked = true;
			}

			if (!already_marked) {
				if (p->coalesced && t == VT_POLYGON) {
					// clean coalesced polygons before simplification to avoid
					// introducing shards between shapes that otherwise would have
					// unioned exactly
					//
					// don't try to scale up because these are still world coordinates
					coalesce_polygon(geom, false);
				}

				// continues to simplify to line_detail even if we have extra detail
				drawvec ngeom = simplify_lines(geom, z, p->tx, p->ty, line_detail, !(prevent[P_CLIPPING] || prevent[P_DUPLICATION]), p->simplification, t == VT_POLYGON ? 4 : 0, shared_nodes, shared_nodes_map, nodepos, shared_nodes_bloom);

				if (p->coalesced && prevent[P_SIMPLIFY_SHARED_NODES]) {
					// do another simplification to eliminate collinearities
					// that were left behind at the former corners between
					// coalesced geometries
					ngeom = simplify_lines(ngeom, z, p->tx, p->ty, line_detail, !(prevent[P_CLIPPING] || prevent[P_DUPLICATION]), 0.1, t == VT_POLYGON ? 4 : 0, shared_nodes, NULL, 0, "");
				}

				if (t != VT_POLYGON || ngeom.size() >= 3) {
					geom = ngeom;
				}
			}
		}
	}

	if (t == VT_LINE && additional[A_REVERSE]) {
		geom = remove_noop(geom, t, 0);
		geom = reorder_lines(geom);
	}

	p->geometry = std::move(geom);
	return area;
}

// This is the worker function that is called from multiple threads to
// simplify and clean the geometry of batches of features.
static void *simplification_worker(void *v) {
	simplification_worker_arg *a = (simplification_worker_arg *) v;
	std::vector<std::shared_ptr<serial_feature>> *features = a->features;

	for (size_t i = a->task; i < (*features).size(); i += a->tasks) {
		double area = 0;
		area = simplify_feature(&*((*features)[i]), *(a->shared_nodes), a->shared_nodes_map, a->nodepos, *(a->shared_nodes_bloom));

		signed char t = (*features)[i]->t;
		int z = (*features)[i]->z;
		int out_detail = (*features)[i]->extra_detail;

		drawvec geom = (*features)[i]->geometry;
		to_tile_scale(geom, z, out_detail);

		if (t == VT_POLYGON) {
			// Scaling may have made the polygon degenerate.
			// Give Clipper a chance to try to fix it.
			{
				drawvec before = geom;

				// we can try scaling up because this is now tile scale
				coalesce_polygon(geom, true);
				if (additional[A_DEBUG_POLYGON]) {
					check_polygon(geom);
				}

				if (geom.size() < 3) {
					if (area > 0) {
						// area is in world coordinates, calculated before scaling down
						geom = revive_polygon(before, area, z, out_detail);
					} else {
						geom.clear();
					}
				}
			}
		}

		if ((*features)[i]->index == 0) {
			(*features)[i]->index = i;
		}
		(*features)[i]->geometry = std::move(geom);
	}

	return NULL;
}

// I really don't understand quite how this feature works any more, which is why I want to
// get rid of the --gamma option. It does something with the feature spacing to calculate
// whether each feature should be kept or is in a dense enough context that it should
// be dropped
int manage_gap(unsigned long long index, unsigned long long *previndex, double scale, double gamma, double *gap) {
	if (gamma > 0) {
		if (*gap > 0) {
			if (index == *previndex) {
				return 1;  // Exact duplicate: can't fulfil the gap requirement
			}

			if (index < *previndex || std::exp(std::log((index - *previndex) / scale) * gamma) >= *gap) {
				// Dot is further from the previous than the nth root of the gap,
				// so produce it, and choose a new gap at the next point.
				*gap = 0;
			} else {
				return 1;
			}
		} else if (index >= *previndex) {
			*gap = (index - *previndex) / scale;

			if (*gap == 0) {
				return 1;  // Exact duplicate: skip
			} else if (*gap < 1) {
				return 1;  // Narrow dot spacing: need to stretch out
			} else {
				*gap = 0;  // Wider spacing than minimum: so pass through unchanged
			}
		}

		*previndex = index;
	}

	return 0;
}

// This function is called to choose the new gap threshold for --drop-densest-as-needed
// and --coalesce-densest-as-needed.
static unsigned long long choose_mingap(std::vector<unsigned long long> &gaps, double f, unsigned long long existing_gap) {
	std::stable_sort(gaps.begin(), gaps.end());

	size_t ix = (gaps.size() - 1) * (1 - f);
	while (ix + 1 < gaps.size() && gaps[ix] <= existing_gap) {
		ix++;
	}

	return gaps[ix];
}

// This function is called to choose the new "extent" threshold to try when a tile exceeds the
// tile size limit or feature limit and `--drop-smallest-as-needed` or `--coalesce-smallest-as-needed`
// has been set.
//
// The "extents" are the areas of the polygon features or the pseudo-areas associated with the
// linestring or point features that were examined for inclusion in the most recent
// iteration of this tile. (This includes features that were dropped because they were below
// the previous size threshold, but not features that were dropped by fractional point dropping).
// The extents are placed in order by the sort, from smallest to largest.
//
// The `fraction` is the proportion of these features that tippecanoe thinks should be retained to
// to make the tile small enough now. Because the extents are sorted from smallest to largest,
// the smallest extent threshold that will retain that fraction of features is found `fraction`
// distance from the end of the list, or at element `(1 - fraction) * (size() - 1)`.
//
// However, the extent found there may be the same extent that was used in the last iteration!
//
// (The "existing_extent" is the extent threshold that selected these features in the recent
// iteration. It is 0 the first time a tile is attempted, and gets higher on successive iterations
// as tippecanoe restricts the features to be kept to larger and larger features.)
//
// The features that are kept are those with a size >= the existing_extent, so if there are a large
// number of features with identical small areas, the new guess may not exclude enough features
// to actually choose a new threshold larger than the previous threshold.
//
// To address this, the array index `ix` of the new chosen extent is incremented toward the end
// of the list, until the possibilities run out or something higher than the old extent is found.
// If there are no higher extents available, the tile has already been reduced as much as possible
// and tippecanoe will exit with an error.
static long long choose_minextent(std::vector<long long> &extents, double f, long long existing_extent) {
	std::stable_sort(extents.begin(), extents.end());

	size_t ix = (extents.size() - 1) * (1 - f);
	while (ix + 1 < extents.size() && extents[ix] <= existing_extent) {
		ix++;
	}

	return extents[ix];
}

static unsigned long long choose_mindrop_sequence(std::vector<unsigned long long> &drop_sequences, double f, unsigned long long existing_drop_sequence) {
	if (drop_sequences.size() == 0) {
		return ULLONG_MAX;
	}

	std::stable_sort(drop_sequences.begin(), drop_sequences.end());

	size_t ix = (drop_sequences.size() - 1) * (1 - f);
	while (ix + 1 < drop_sequences.size() && drop_sequences[ix] <= existing_drop_sequence) {
		ix++;
	}

	return drop_sequences[ix];
}

static double choose_minattribute(std::vector<double> &attribute_values, double f, double existing_attribute, bool descending) {
	if (attribute_values.size() == 0) {
		return existing_attribute;
	}

	std::stable_sort(attribute_values.begin(), attribute_values.end());

	if (descending) {
		// For descending: drop features > threshold, keep features <= threshold
		// ix points at the last value to keep
		size_t ix = (size_t)((attribute_values.size() - 1) * f);
		while (ix > 0 && attribute_values[ix] >= existing_attribute) {
			ix--;
		}

		if (attribute_values[ix] >= existing_attribute) {
			return existing_attribute;
		}

		return attribute_values[ix];
	} else {
		// For ascending: drop features < threshold, keep features >= threshold
		// ix points at the first value to keep
		size_t ix = (size_t)ceil((double)(attribute_values.size() - 1) * (1 - f));
		if (ix >= attribute_values.size()) {
			ix = attribute_values.size() - 1;
		}
		while (ix + 1 < attribute_values.size() && attribute_values[ix] <= existing_attribute) {
			ix++;
		}

		if (attribute_values[ix] <= existing_attribute) {
			return existing_attribute;
		}

		return attribute_values[ix];
	}
}

static unsigned long long calculate_drop_sequence(serial_feature const &sf) {
	unsigned long long zoom = std::min(std::max((unsigned long long) sf.feature_minzoom, 0ULL), 31ULL);
	unsigned long long out = zoom << (64 - 5);	      // top bits are the zoom level: top-priority features are those that appear in the low zooms
	out |= bit_reverse(sf.index) & ~(31ULL << (64 - 5));  // remaining bits are from the inverted indes, which should incrementally fill in spatially
	return ~out;					      // lowest numbered feature gets dropped first
}

struct task {
	int fileno = 0;
	size_t todo;

	bool operator<(const struct task &o) const {
		return todo < o.todo;
	}
};

// This is the block of parameters that are passed to write_tile() to read a tile
// from the serialized form, do whatever needs to be done to it, and to write the
// MVT-format output to the output tileset.
//
// The _out parameters are thresholds calculated during tiling; they are collected
// by the caller to determine whether the zoom level needs to be done over with
// new thresholds.
struct write_tile_args {
	int threadno;
	std::vector<task *> *tasks;
	char *global_stringpool = NULL;
	int min_detail = 0;
	sqlite3 *outdb = NULL;
	const char *outdir = NULL;
	int buffer = 0;
	const char *fname = NULL;
	compressor **geomfile = NULL;
	std::atomic<long long> *geompos = NULL;
	double todo = 0;
	std::atomic<long long> *along = NULL;
	double gamma = 0;
	double gamma_out = 0;
	int child_shards = 0;
	int *geomfd = NULL;
	off_t *geom_size = NULL;
	std::atomic<unsigned> *midx = NULL;
	std::atomic<unsigned> *midy = NULL;
	int maxzoom = 0;
	int minzoom = 0;
	int basezoom = 0;
	double droprate = 0;
	int full_detail = 0;
	int low_detail = 0;
	double simplification = 0;
	std::atomic<long long> *most = NULL;
	long long *pool_off = NULL;
	unsigned *initial_x = NULL;
	unsigned *initial_y = NULL;
	std::atomic<int> *running = NULL;
	int err = 0;
	std::vector<std::map<std::string, layermap_entry>> *layermaps = NULL;
	std::vector<std::vector<std::string>> *layer_unmaps = NULL;
	size_t pass = 0;
	unsigned long long mingap = 0;
	unsigned long long mingap_out = 0;
	long long minextent = 0;
	long long minextent_out = 0;
	unsigned long long mindrop_sequence = 0;
	unsigned long long mindrop_sequence_out = 0;
	// the infinity that excludes nothing, for the ascending order that
	// drop_by_attribute_descending also defaults to. dropping_features()
	// reads these, so they must not start out at a value that looks like
	// a threshold that has already been chosen.
	double minattribute = -HUGE_VAL;
	double minattribute_out = -HUGE_VAL;
	std::string const *drop_by_attribute_as_needed_attribute = NULL;
	bool drop_by_attribute_descending = false;
	size_t tile_size_out = 0;
	size_t feature_count_out = 0;
	const char *prefilter = NULL;
	const char *postfilter = NULL;
	std::unordered_map<std::string, attribute_op> const *attribute_accum = NULL;
	bool still_dropping = false;
	int wrote_zoom = 0;
	size_t tiling_seg = 0;
	json_object *filter = NULL;
	std::vector<std::string> const *unidecode_data;
	std::atomic<size_t> *dropped_count = NULL;
	atomic_strategy *strategy = NULL;
	int zoom = -1;
	bool compressed;
	node *shared_nodes_map;
	size_t nodepos;
	std::string const *shared_nodes_bloom;
	std::set<zxy> const *skip_children;  // what is being skipped at this zoom
	std::set<zxy> skip_children_out;     // what will be skipped in the next zoom
	bool first_dropping_pass = false;    // is this the first pass of this zoom that is dropping features?
};

// Is this zoom level discarding features to make its tiles fit? The thresholds
// start out excluding nothing, and only move inward once some tile in the zoom
// fails to fit and the whole zoom is retried with a threshold for discarding
// features. For mingap, minextent, and mindrop_sequence that starting point is
// zero; for minattribute it is the infinity on whichever side is being kept.
//
// Pyramid truncation is disabled while this is true, so that every tile in the
// zoom is reduced consistently, and tiles that were skipped because an ancestor
// truncated its pyramid are revived.
static bool dropping_features(unsigned long long mingap, long long minextent, unsigned long long mindrop_sequence, double minattribute, bool drop_by_attribute_descending) {
	bool dropping_by_attribute = drop_by_attribute_descending
					     ? minattribute != HUGE_VAL
					     : minattribute != -HUGE_VAL;

	return mingap != 0 || minextent != 0 || mindrop_sequence != 0 || dropping_by_attribute;
}

// Clips a feature's geometry to the tile bounds at the specified zoom level
// with the specified buffer. Returns true if the feature was entirely clipped away
// by bounding box alone; otherwise returns false.
static bool clip_to_tile(serial_feature &sf, int z, long long buffer) {
	int quick = quick_check(sf.bbox, z, buffer);

	if (z == 0) {
		if (sf.bbox[0] <= (1LL << 32) * buffer / 256 || sf.bbox[2] >= (1LL << 32) - ((1LL << 32) * buffer / 256)) {
			// If the geometry extends off the edge of the world, concatenate on another copy
			// shifted by 360 degrees, and then make sure both copies get clipped down to size.

			size_t n = sf.geometry.size();

			if (sf.bbox[0] <= (1LL << 32) * buffer / 256) {
				for (size_t i = 0; i < n; i++) {
					sf.geometry.push_back(draw(sf.geometry[i].op, sf.geometry[i].x + (1LL << 32), sf.geometry[i].y));
				}
			}

			if (sf.bbox[2] >= (1LL << 32) - ((1LL << 32) * buffer / 256)) {
				for (size_t i = 0; i < n; i++) {
					sf.geometry.push_back(draw(sf.geometry[i].op, sf.geometry[i].x - (1LL << 32), sf.geometry[i].y));
				}
			}

			sf.bbox[0] = 0;
			sf.bbox[2] = 1LL << 32;

			quick = -1;
		}
	}

	if (quick == 0) {  // entirely outside the tile
		return true;
	}

	// if quick == 3 the feature touches the buffer, not just the tile proper,
	// so we need to clip to add intersection points at the tile edge.

	// if quick == 2 it touches the buffer and beyond, so likewise

	// if quick == 1 we should be able to get away without clipping, because
	// the feature is entirely within the tile proper.

	// Can't accept the quick check if guaranteeing no duplication, since the
	// overlap might have been in the buffer.
	if (quick != 1 || prevent[P_DUPLICATION]) {
		drawvec clipped;

		// Do the clipping, even if we are going to include the whole feature,
		// so that we can know whether the feature itself, or only the feature's
		// bounding box, touches the tile.

		if (sf.t == VT_LINE) {
			clipped = clip_lines(sf.geometry, z, buffer);
		}
		if (sf.t == VT_POLYGON) {
			clipped = simple_clip_poly(sf.geometry, z, buffer, sf.edge_nodes, prevent[P_SIMPLIFY_SHARED_NODES]);
		}
		if (sf.t == VT_POINT) {
			clipped = clip_point(sf.geometry, z, buffer);
		}

		clipped = remove_noop(clipped, sf.t, 0);

		// Must clip at z0 even if we don't want clipping, to handle features
		// that are duplicated across the date line

		if (prevent[P_DUPLICATION] && z != 0) {
			if (point_within_tile((sf.bbox[0] + sf.bbox[2]) / 2, (sf.bbox[1] + sf.bbox[3]) / 2, z)) {
				// sf.geometry is unchanged
			} else {
				sf.geometry.clear();
			}
		} else if (prevent[P_CLIPPING] && z != 0) {
			if (clipped.size() == 0) {
				sf.geometry.clear();
			} else {
				// sf.geometry is unchanged
			}
		} else {
			sf.geometry = clipped;
		}
	}

	return false;
}

// Removes the attributes named in --exclude, if any, from the feature
static void remove_attributes(serial_feature &sf, std::set<std::string> const &exclude_attributes) {
	for (ssize_t i = sf.keys.size() - 1; i >= 0; i--) {
		std::string key = sf.stringpool + sf.keys[i] + 1;
		if (exclude_attributes.count(key) > 0) {
			sf.keys.erase(sf.keys.begin() + i);
			sf.values.erase(sf.values.begin() + i);
		}
	}

	for (ssize_t i = sf.full_keys.size() - 1; i >= 0; i--) {
		std::string key = *sf.full_keys[i];
		if (exclude_attributes.count(key) > 0) {
			sf.full_keys.erase(sf.full_keys.begin() + i);
			sf.full_values.erase(sf.full_values.begin() + i);
		}
	}
}

// This map maintains the count for attributes that resulted from the "mean"
// --accumulate-attribute option so that features' attributes can be averaged in
// without knowing their total count in advance.
struct multiplier_state {
	std::map<std::string, int> count;
};

static bool skip_next_feature(decompressor *geoms, std::atomic<long long> *geompos_in, bool compressed) {
	long long len;

	if (geoms->deserialize_long_long(&len, geompos_in) == 0) {
		fprintf(stderr, "Unexpected physical EOF in feature stream\n");
		exit(EXIT_READ);
	}
	if (len <= 0) {
		if (compressed) {
			geoms->end(geompos_in);
		}

		return false;
	}

	std::string s;
	s.resize(len);
	size_t n = geoms->fread((void *) s.c_str(), sizeof(char), s.size(), geompos_in);
	if (n != s.size()) {
		fprintf(stderr, "Short read (%zu for %zu) from geometry\n", n, s.size());
		exit(EXIT_READ);
	}

	return true;
}

struct next_feature_state {
	unsigned long long previndex = 0;
	unsigned long long prev_not_dropped_index = 0;
	// set when a feature excluded by tippecanoe_minzoom first appears beyond the next
	// zoom, so variable-depth pyramids don't leaf above the feature's minzoom and prune it away
	bool minzoom_feature_pending = false;
};

// This function is called repeatedly from write_tile() to retrieve the next feature
// from the input stream. If the stream is at an end, it returns a feature with the
// geometry type set to -2.
static serial_feature next_feature(decompressor *geoms, std::atomic<long long> *geompos_in, int z, unsigned tx, unsigned ty, unsigned *initial_x, unsigned *initial_y, long long *original_features, long long *unclipped_features, int nextzoom, int maxzoom, int minzoom, int max_zoom_increment, size_t pass, std::atomic<long long> *along, long long alongminus, int buffer, std::atomic<bool> *within, compressor **geomfile, std::atomic<long long> *geompos, long long start_geompos[], std::atomic<double> *oprogress, double todo, const char *fname, int child_shards, json_object *filter, const char *global_stringpool, long long *pool_off, std::vector<std::vector<std::string>> *layer_unmaps, bool first_time, bool write_children, bool compressed, multiplier_state *multiplier_state, std::shared_ptr<std::string> &tile_stringpool, std::vector<std::string> const &unidecode_data, next_feature_state &next_feature_state, double droprate) {
	double extra_multiplier_zooms = log(retain_points_multiplier) / log(droprate);

	while (1) {
		serial_feature sf;
		long long len;

		if (geoms->deserialize_long_long(&len, geompos_in) == 0) {
			fprintf(stderr, "Unexpected physical EOF in feature stream\n");
			exit(EXIT_READ);
		}
		if (len <= 0) {
			if (compressed) {
				geoms->end(geompos_in);
			}

			sf.t = -2;
			return sf;
		}

		std::string s;
		s.resize(len);
		size_t n = geoms->fread((void *) s.c_str(), sizeof(char), s.size(), geompos_in);
		if (n != s.size()) {
			fprintf(stderr, "Short read (%zu for %zu) from geometry\n", n, s.size());
			exit(EXIT_READ);
		}

		sf = deserialize_feature(s, z, tx, ty, initial_x, initial_y);
		sf.stringpool = global_stringpool + pool_off[sf.segment];

		// with fractional zoom level, so we can target a specific number
		// of features to keep with retain-points-multiplier, not just the
		// powers of the drop rate
		//
		// the shift-right is because we lose the bottom two bits of
		// point coordinate precision in the calculation in serial.cpp.
		//
		// the fractional part should actually be scaled in an exponential
		// curve instead of linear, but I can't figure out how to make it
		// come out right, and this should be close enough.
		double feature_minzoom = sf.feature_minzoom - (bit_reverse(sf.index >> 2) / pow(2, 64));

		size_t passes = pass + 1;
		double progress = floor(((((*geompos_in + *along - alongminus) / (double) todo) + pass) / passes + z) / (maxzoom + 1) * 1000) / 10;
		if (progress >= *oprogress + 0.1) {
			if (!quiet && !quiet_progress && progress_time()) {
				fprintf(stderr, "  %3.1f%%  %d/%u/%u  \r", progress, z, tx, ty);
				fflush(stderr);
			}
			if (logger.json_enabled && progress_time()) {
				logger.progress_tile(progress);
			}
			*oprogress = progress;
		}

		(*original_features)++;

		if (sf.gap == 0) {
			if (sf.index != next_feature_state.previndex) {
				long long ox = (1LL << (32 - z)) * tx;
				long long oy = (1LL << (32 - z)) * ty;

				unsigned wx1, wy1;
				decode_index(next_feature_state.previndex, &wx1, &wy1);

				for (auto const &g : sf.geometry) {
					long long dx = (long long) wx1 - (g.x + ox);
					long long dy = (long long) wy1 - (g.y + oy);

					unsigned long long d = dx * dx + dy * dy;
					if (d > sf.gap) {
						sf.gap = d;
					}
				}
			}
		}
		next_feature_state.previndex = sf.index;

		if (clip_to_tile(sf, z, buffer)) {
			continue;
		}

		if (sf.geometry.size() > 0) {
			(*unclipped_features)++;
		} else {
			// XXX should continue, but affects test outputs
		}

		if (first_time && write_children) { /* only write out the next zoom once, even if we retry */
			if (sf.tippecanoe_maxzoom == -1 || sf.tippecanoe_maxzoom >= nextzoom) {
				rewrite(sf, z, nextzoom, maxzoom, tx, ty, buffer, within, geompos, start_geompos, geomfile, fname, child_shards, max_zoom_increment, sf.segment, initial_x, initial_y);
			}
		}

		if (z < minzoom) {
			continue;
		}

		if (sf.tippecanoe_minzoom != -1 && z < sf.tippecanoe_minzoom) {
			// a leaf at z carries only z-visible content, so an excluded feature at
			// any deeper minzoom (even z + 1) must block leafing here
			next_feature_state.minzoom_feature_pending = true;
			continue;
		}
		if (sf.tippecanoe_maxzoom != -1 && z > sf.tippecanoe_maxzoom) {
			continue;
		}

		if (filter != NULL) {
			std::unordered_map<std::string, mvt_value> attributes;
			std::string &layername = (*layer_unmaps)[sf.segment][sf.layer];
			std::set<std::string> exclude_attributes;

			for (size_t i = 0; i < sf.keys.size(); i++) {
				std::string key = sf.stringpool + sf.keys[i] + 1;

				serial_val sv;
				sv.type = sf.stringpool[sf.values[i]];
				sv.s = sf.stringpool + sf.values[i] + 1;

				mvt_value val = stringified_to_mvt_value(sv.type, sv.s.c_str(), tile_stringpool);
				attributes.insert(std::pair<std::string, mvt_value>(key, val));
			}

			for (size_t i = 0; i < sf.full_keys.size(); i++) {
				std::string key = *sf.full_keys[i];
				mvt_value val = stringified_to_mvt_value(sf.full_values[i].type, sf.full_values[i].s.c_str(), tile_stringpool);

				attributes.insert(std::pair<std::string, mvt_value>(key, val));
			}

			if (sf.has_id) {
				mvt_value v;
				v.type = mvt_uint;
				v.numeric_value.uint_value = sf.id;

				attributes.insert(std::pair<std::string, mvt_value>("$id", v));
			}

			mvt_value v;
			v.type = mvt_string;

			if (sf.t == mvt_point) {
				v.set_string_value("Point");
			} else if (sf.t == mvt_linestring) {
				v.set_string_value("LineString");
			} else if (sf.t == mvt_polygon) {
				v.set_string_value("Polygon");
			}

			attributes.insert(std::pair<std::string, mvt_value>("$type", v));

			mvt_value v2;
			v2.type = mvt_uint;
			v2.numeric_value.uint_value = z;

			attributes.insert(std::pair<std::string, mvt_value>("$zoom", v2));

			if (!evaluate(attributes, layername, filter, exclude_attributes, unidecode_data)) {
				continue;
			}

			if (exclude_attributes.size() > 0) {
				remove_attributes(sf, exclude_attributes);
			}
		}

		if (sf.tippecanoe_minzoom == -1) {
			sf.dropped = FEATURE_DROPPED;  // dropped

			std::string &layername = (*layer_unmaps)[sf.segment][sf.layer];
			auto count = multiplier_state->count.find(layername);
			if (count == multiplier_state->count.end()) {
				multiplier_state->count.emplace(layername, 0);
				count = multiplier_state->count.find(layername);
				sf.dropped = FEATURE_KEPT;  // the first feature in each tile is always kept
			}

			if (z >= feature_minzoom || sf.dropped == FEATURE_KEPT) {
				count->second = 0;
				sf.dropped = FEATURE_KEPT;  // feature is kept
			} else if (z + extra_multiplier_zooms >= feature_minzoom && count->second + 1 < retain_points_multiplier) {
				count->second++;
				sf.dropped = count->second;
			} else if (preserve_multiplier_density_threshold > 0 &&
				   sf.index - next_feature_state.prev_not_dropped_index > ((1LL << (32 - z)) / preserve_multiplier_density_threshold) * ((1LL << (32 - z)) / preserve_multiplier_density_threshold)) {
				sf.dropped = FEATURE_ADDED_FOR_MULTIPLIER_DENSITY;
			} else {
				sf.dropped = FEATURE_DROPPED;
			}
		} else {
			sf.dropped = FEATURE_KEPT;
		}

		if (sf.dropped != FEATURE_DROPPED) {
			next_feature_state.prev_not_dropped_index = sf.index;
		}

		// Remove nulls, now that the expression evaluation filter has run

		for (ssize_t i = (ssize_t) sf.keys.size() - 1; i >= 0; i--) {
			int type = sf.stringpool[sf.values[i]];

			if (type == mvt_null) {
				sf.keys.erase(sf.keys.begin() + i);
				sf.values.erase(sf.values.begin() + i);
			}
		}

		for (ssize_t i = (ssize_t) sf.full_keys.size() - 1; i >= 0; i--) {
			if (sf.full_values[i].type == mvt_null) {
				sf.full_keys.erase(sf.full_keys.begin() + i);
				sf.full_values.erase(sf.full_values.begin() + i);
			}
		}

		return sf;
	}
}

struct run_prefilter_args {
	decompressor *geoms = NULL;
	std::atomic<long long> *geompos_in = NULL;
	int z = 0;
	unsigned tx = 0;
	unsigned ty = 0;
	unsigned *initial_x = 0;
	unsigned *initial_y = 0;
	long long *original_features = 0;
	long long *unclipped_features = 0;
	int nextzoom = 0;
	int maxzoom = 0;
	int minzoom = 0;
	int max_zoom_increment = 0;
	size_t pass = 0;
	std::atomic<long long> *along = 0;
	long long alongminus = 0;
	int buffer = 0;
	std::atomic<bool> *within = NULL;
	compressor **geomfile = NULL;
	std::atomic<long long> *geompos = NULL;
	long long *start_geompos = NULL;
	std::atomic<double> *oprogress = NULL;
	double todo = 0;
	const char *fname = 0;
	int child_shards = 0;
	std::vector<std::vector<std::string>> *layer_unmaps = NULL;
	char *global_stringpool = NULL;
	long long *pool_off = NULL;
	FILE *prefilter_fp = NULL;
	json_object *filter = NULL;
	std::vector<std::string> const *unidecode_data;
	bool first_time = false;
	bool write_children = false;
	bool compressed = false;
	double droprate = 1;
};

void *run_prefilter(void *v) {
	run_prefilter_args *rpa = (run_prefilter_args *) v;
	json_writer state(rpa->prefilter_fp);
	struct multiplier_state multiplier_state;
	std::shared_ptr<std::string> tile_stringpool = std::make_shared<std::string>();
	next_feature_state next_feature_state;

	while (1) {
		serial_feature sf = next_feature(rpa->geoms, rpa->geompos_in, rpa->z, rpa->tx, rpa->ty, rpa->initial_x, rpa->initial_y, rpa->original_features, rpa->unclipped_features, rpa->nextzoom, rpa->maxzoom, rpa->minzoom, rpa->max_zoom_increment, rpa->pass, rpa->along, rpa->alongminus, rpa->buffer, rpa->within, rpa->geomfile, rpa->geompos, rpa->start_geompos, rpa->oprogress, rpa->todo, rpa->fname, rpa->child_shards, rpa->filter, rpa->global_stringpool, rpa->pool_off, rpa->layer_unmaps, rpa->first_time, rpa->write_children, rpa->compressed, &multiplier_state, tile_stringpool, *(rpa->unidecode_data), next_feature_state, rpa->droprate);
		if (sf.t < 0) {
			break;
		}

		mvt_layer tmp_layer;
		tmp_layer.extent = 1LL << 32;
		tmp_layer.name = (*(rpa->layer_unmaps))[sf.segment][sf.layer];

		if (sf.t == VT_POLYGON) {
			sf.geometry = close_poly(sf.geometry);
		}

		mvt_feature tmp_feature;
		tmp_feature.type = sf.t;
		tmp_feature.geometry = to_feature(sf.geometry);
		tmp_feature.id = sf.id;
		tmp_feature.has_id = sf.has_id;
		tmp_feature.dropped = sf.dropped;

		// Offset from tile coordinates back to world coordinates
		unsigned sx = 0, sy = 0;
		if (rpa->z != 0) {
			sx = rpa->tx << (32 - rpa->z);
			sy = rpa->ty << (32 - rpa->z);
		}
		for (size_t i = 0; i < tmp_feature.geometry.size(); i++) {
			tmp_feature.geometry[i].x += sx;
			tmp_feature.geometry[i].y += sy;
		}

		decode_meta(sf, tmp_layer, tmp_feature);
		tmp_layer.features.push_back(tmp_feature);

		layer_to_geojson(tmp_layer, 0, 0, 0, false, true, false, true, sf.index, sf.seq, sf.extent, true, state, 0, std::set<std::string>());
	}

	if (fclose(rpa->prefilter_fp) != 0) {
		if (errno == EPIPE) {
			static bool warned = false;
			if (!warned) {
				fprintf(stderr, "Warning: broken pipe in prefilter\n");
				warned = true;
			}
		} else {
			perror("fclose output to prefilter");
			exit(EXIT_CLOSE);
		}
	}
	return NULL;
}

void add_tilestats(std::string const &layername, int z, std::vector<std::map<std::string, layermap_entry>> *layermaps, size_t tiling_seg, std::vector<std::vector<std::string>> *layer_unmaps, std::string const &key, serial_val const &val) {
	std::map<std::string, layermap_entry> &layermap = (*layermaps)[tiling_seg];
	if (layermap.count(layername) == 0) {
		layermap_entry lme = layermap_entry(layermap.size());
		lme.minzoom = z;
		lme.maxzoom = z;
		lme.retain = 1;

		layermap.insert(std::pair<std::string, layermap_entry>(layername, lme));

		if (lme.id >= (*layer_unmaps)[tiling_seg].size()) {
			(*layer_unmaps)[tiling_seg].resize(lme.id + 1);
			(*layer_unmaps)[tiling_seg][lme.id] = layername;
		}
	}
	auto ts = layermap.find(layername);
	if (ts == layermap.end()) {
		fprintf(stderr, "Internal error: layer %s not found\n", layername.c_str());
		exit(EXIT_IMPOSSIBLE);
	}

	add_to_tilestats(ts->second.tilestats, key, val);
}

void promote_attribute(std::string const &key, serial_feature &p, key_pool &key_pool) {
	// If the feature being merged into has this key as a metadata reference,
	// promote it to a full_key so it can be modified

	for (size_t i = 0; i < p.keys.size(); i++) {
		if (strcmp(key.c_str(), p.stringpool + p.keys[i] + 1) == 0) {
			serial_val sv;
			sv.s = p.stringpool + p.values[i] + 1;
			sv.type = p.stringpool[p.values[i]];

			p.full_keys.push_back(key_pool.pool(key));
			p.full_values.push_back(std::move(sv));

			p.keys.erase(p.keys.begin() + i);
			p.values.erase(p.values.begin() + i);

			break;
		}
	}
}

void promote_attribute_prefix(std::string const &key, std::string const &prefixed_key, serial_feature &p, key_pool &key_pool) {
	// does the prefixed attribute already exist as a full key?
	ssize_t found_as = -1;
	for (size_t i = 0; i < p.full_keys.size(); i++) {
		if (prefixed_key == *p.full_keys[i]) {
			// yes, so we're done
			return;
		}
		if (key == *p.full_keys[i]) {
			found_as = i;
		}
	}

	// or did we find the source as a full key? then copy it
	if (found_as >= 0) {
		p.full_keys.push_back(key_pool.pool(prefixed_key));
		p.full_values.push_back(p.full_values[found_as]);

		return;
	}

	// does the prefix attribute exist as a reference key?
	found_as = -1;
	for (size_t i = 0; i < p.keys.size(); i++) {
		if (strcmp(prefixed_key.c_str(), p.stringpool + p.keys[i] + 1) == 0) {
			// yes, so promote it to a full key
			serial_val sv;
			sv.s = p.stringpool + p.values[i] + 1;
			sv.type = p.stringpool[p.values[i]];

			p.full_keys.push_back(key_pool.pool(prefixed_key));
			p.full_values.push_back(std::move(sv));

			p.keys.erase(p.keys.begin() + i);
			p.values.erase(p.values.begin() + i);

			return;
		} else if (strcmp(key.c_str(), p.stringpool + p.keys[i] + 1) == 0) {
			found_as = i;
		}
	}

	// or did we find the source as a reference key? then copy it
	if (found_as >= 0) {
		serial_val sv;
		sv.s = p.stringpool + p.values[found_as] + 1;
		sv.type = p.stringpool[p.values[found_as]];

		p.full_keys.push_back(key_pool.pool(prefixed_key));
		p.full_values.push_back(std::move(sv));

		return;
	}

	// it does not exist, so preserve_attribute() will create it
}

// accumulate attribute values from sf onto p
void preserve_attributes(std::unordered_map<std::string, attribute_op> const *attribute_accum, const serial_feature &sf, serial_feature &p, key_pool &key_pool) {
	for (size_t i = 0; i < sf.keys.size(); i++) {
		std::string key = sf.stringpool + sf.keys[i] + 1;

		auto f = attribute_accum->find(key);
		if (f != attribute_accum->end()) {
			serial_val sv;
			sv.type = sf.stringpool[sf.values[i]];
			sv.s = sf.stringpool + sf.values[i] + 1;

			promote_attribute(key, p, key_pool);
			preserve_attribute(f->second, key, sv, p.full_keys, p.full_values, key_pool);
		}
	}
	for (size_t i = 0; i < sf.full_keys.size(); i++) {
		const std::string key = *sf.full_keys[i];

		auto f = attribute_accum->find(key);
		if (f != attribute_accum->end()) {
			const serial_val &sv = sf.full_values[i];

			promote_attribute(key, p, key_pool);  // promotes it in the target feature
			preserve_attribute(f->second, key, sv, p.full_keys, p.full_values, key_pool);
		}
	}
}

// This function finds the feature in `features` onto which the attributes or geometry
// of a feature that is being dropped (`sf`) will be accumulated or coalesced. It
// ordinarily returns the most recently-added feature from the same layer as the feature
// that is being dropped.
//
bool find_feature_to_accumulate_onto(std::vector<std::shared_ptr<serial_feature>> &features, serial_feature &sf, ssize_t &out, std::vector<std::vector<std::string>> *layer_unmaps, long long maxextent) {
	for (size_t i = features.size(); i > 0; i--) {
		if (features[i - 1]->t == sf.t) {
			std::string &layername1 = (*layer_unmaps)[features[i - 1]->segment][features[i - 1]->layer];
			std::string &layername2 = (*layer_unmaps)[sf.segment][sf.layer];

			if (layername1 == layername2 && features[i - 1]->extent <= maxextent) {
				out = i - 1;
				return true;
			}
		}
	}

	return false;
}

// Keep only a sample of 100K extents for feature dropping,
// to avoid spending lots of memory on a complete list when there are
// hundreds of millions of features.
template <class T>
void add_sample_to(std::vector<T> &vals, T val, size_t &increment, size_t seq) {
	if (seq % increment == 0) {
		vals.push_back(val);

		if (vals.size() > 100000) {
			std::vector<T> tmp;

			for (size_t i = 0; i < vals.size(); i += 2) {
				tmp.push_back(vals[i]);
			}

			increment *= 2;
			vals = tmp;
		}
	}
}

void coalesce_geometry(serial_feature &p, serial_feature &sf) {
// XXX need another way to deduplicate here
#if 0
// if the geometry being coalesced on is an exact duplicate
// of an existing geometry, just drop it

for (size_t i = 0; i < p.geometries.size(); i++) {
if (p.geometries[i] == sf.geometry) {
return;
}
}
#endif

	size_t s = p.geometry.size();
	p.geometry.resize(s + sf.geometry.size());
	for (size_t i = 0; i < sf.geometry.size(); i++) {
		p.geometry[s + i] = sf.geometry[i];
	}
}

// This is the structure that the features from each layer are accumulated into
struct layer_features {
	std::vector<std::shared_ptr<serial_feature>> features;	// The features of this layer, so far
	size_t multiplier_cluster_size = 0;			// The feature count of the current multiplier cluster
};

bool drop_feature_unless_it_can_be_added_to_a_multiplier_cluster(layer_features &layer, serial_feature &sf, std::vector<std::vector<std::string>> *layer_unmaps, strategy &strategy, bool &drop_rest, std::unordered_map<std::string, attribute_op> const *attribute_accum, key_pool &key_pool) {
	ssize_t which_serial_feature;

	if (find_feature_to_accumulate_onto(layer.features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
		strategy.dropped_as_needed++;
		if (layer.multiplier_cluster_size < (size_t) retain_points_multiplier) {
			// we have capacity to keep this feature as part of an existing multiplier cluster that isn't full yet
			// so do that instead of dropping it
			sf.dropped = layer.multiplier_cluster_size + 1;
			return false;  // converted rather than dropped
		} else {
			preserve_attributes(attribute_accum, sf, *layer.features[which_serial_feature], key_pool);
			drop_rest = true;
			return true;  // dropped
		}
	}

	return false;  // did not drop because nothing could be found to accumulate attributes onto
}

void skip_tile(decompressor *geoms, std::atomic<long long> *geompos_in, bool compressed_input) {
	while (skip_next_feature(geoms, geompos_in, compressed_input)) {
		;
	}
}

long long write_tile(decompressor *geoms, std::atomic<long long> *geompos_in, char *global_stringpool, int z, const unsigned tx, const unsigned ty, const int detail, int min_detail, sqlite3 *outdb, const char *outdir, int buffer, const char *fname, compressor **geomfile, std::atomic<long long> *geompos, int minzoom, int maxzoom, double todo, std::atomic<long long> *along, long long alongminus, double gamma, int child_shards, long long *pool_off, unsigned *initial_x, unsigned *initial_y, std::atomic<int> *running, double simplification, std::vector<std::map<std::string, layermap_entry>> *layermaps, std::vector<std::vector<std::string>> *layer_unmaps, size_t tiling_seg, size_t pass, unsigned long long mingap, long long minextent, unsigned long long mindrop_sequence, double minattribute, const char *prefilter, const char *postfilter, json_object *filter, write_tile_args *arg, atomic_strategy *strategy_out, bool compressed_input, node *shared_nodes_map, size_t nodepos, std::string const &shared_nodes_bloom, std::vector<std::string> const &unidecode_data, long long estimated_complexity, bool write_children, std::set<zxy> &skip_children_out) {
	double merge_fraction = 1;
	double mingap_fraction = 1;
	double minextent_fraction = 1;
	double mindrop_sequence_fraction = 1;
	double minattribute_fraction = 1;

	static std::atomic<double> oprogress(0);
	long long og = *geompos_in;

	// XXX is there a way to do this without floating point?
	int max_zoom_increment = std::log(child_shards) / std::log(4);
	if (child_shards < 4 || max_zoom_increment < 1) {
		fprintf(stderr, "Internal error: %d shards, max zoom increment %d\n", child_shards, max_zoom_increment);
		exit(EXIT_IMPOSSIBLE);
	}
	if ((((child_shards - 1) << 1) & child_shards) != child_shards) {
		fprintf(stderr, "Internal error: %d shards not a power of 2\n", child_shards);
		exit(EXIT_IMPOSSIBLE);
	}

	int nextzoom = z + 1;
	if (nextzoom < minzoom) {
		if (z + max_zoom_increment > minzoom) {
			nextzoom = minzoom;
		} else {
			nextzoom = z + max_zoom_increment;
		}
	}

	// only for -K
	unsigned long long cluster_mingap = ((1LL << (32 - z)) / 256 * cluster_distance) * ((1LL << (32 - z)) / 256 * cluster_distance);

	int first_detail = detail, second_detail = detail - 1;
	bool trying_to_stop_early = false;
	bool can_stop_early = true;
	if (additional[A_VARIABLE_DEPTH_PYRAMID] && !dropping_features(mingap, minextent, mindrop_sequence, minattribute, arg->drop_by_attribute_descending)) {
		// If we are trying to stop early, there is an extra first pass with full+extra detail,
		// and which loops if everything doesn't fit rather than trying to drop or union features.

		// empirical estimate from ne_10m_admin_0_countries, CPAD units, Cal fires.
		// only try to make an overzoomable final tile if it seems like it might work
		long long estimated_output_tile_size = 0.6693 * estimated_complexity - 3.36e+04;
		if (estimated_output_tile_size < (long long) (0.9 * max_tile_size) && 30 - z > detail) {
			first_detail = 30 - z;
			second_detail = detail;
			trying_to_stop_early = true;
		}
	}

	size_t detail_reduced = 0;
	bool first_time = true;
	// This only loops if the tile data didn't fit, in which case the detail
	// goes down and the progress indicator goes backward for the next try.
	for (int line_detail = first_detail;
	     line_detail >= min_detail || line_detail == detail;
	     line_detail = line_detail == first_detail ? second_detail : line_detail - 1) {
		oprogress = 0;

		long long count = 0;
		double accum_area = 0;

		unsigned long long previndex = 0, density_previndex = 0, merge_previndex = 0;
		unsigned long long extent_previndex = 0;
		double scale = (double) (1LL << (64 - 2 * (z + 8)));
		double gap = 0, density_gap = 0;
		double spacing = 0;

		long long original_features = 0;
		long long unclipped_features = 0;

		std::map<std::string, layer_features> layers;

		std::vector<unsigned long long> gaps;
		size_t gaps_increment = 1;
		std::vector<long long> extents;
		size_t extents_increment = 1;
		std::vector<unsigned long long> drop_sequences;
		size_t drop_sequences_increment = 1;
		std::vector<double> attribute_values;
		size_t attribute_values_increment = 1;

		double coalesced_area = 0;
		drawvec shared_nodes;

		int tile_detail = line_detail;
		size_t skipped = 0;
		size_t kept = 0;

		size_t unsimplified_geometry_size = 0;
		size_t simplified_geometry_through = 0;

		size_t lead_features_count = 0;			     // of the tile so far
		size_t other_multiplier_cluster_features_count = 0;  // of the tile so far

		bool too_many_features = false;
		bool too_many_bytes = false;

		key_pool key_pool;

		std::vector<std::atomic<bool> > within(child_shards);
		std::vector<long long> start_geompos(child_shards);
		for (size_t i = 0; i < (size_t) child_shards; i++) {
			within[i] = false;
			start_geompos[i] = -1;
		}

		std::shared_ptr<std::string> tile_stringpool = std::make_shared<std::string>();

		if (*geompos_in != og) {
			if (compressed_input) {
				if (geoms->within) {
					geoms->end(geompos_in);
				}

				geoms->begin();
			}

			if (fseek(geoms->fp, og, SEEK_SET) != 0) {
				perror("fseek geom");
				exit(EXIT_SEEK);
			}

			*geompos_in = og;
			geoms->zs.avail_in = 0;
			geoms->zs.avail_out = 0;
		}

		int prefilter_write = -1, prefilter_read = -1;
		pid_t prefilter_pid = 0;
		FILE *prefilter_fp = NULL;
		pthread_t prefilter_writer;
		run_prefilter_args rpa;	 // here so it stays in scope until joined
		FILE *prefilter_read_fp = NULL;
		json_pull *prefilter_jp = NULL;

		if (z < minzoom) {
			prefilter = NULL;
			postfilter = NULL;
		}

		if (prefilter != NULL) {
			setup_filter(prefilter, &prefilter_write, &prefilter_read, &prefilter_pid, z, tx, ty);
			prefilter_fp = fdopen(prefilter_write, "w");
			if (prefilter_fp == NULL) {
				perror("freopen prefilter");
				exit(EXIT_OPEN);
			}

			rpa.geoms = geoms;
			rpa.geompos_in = geompos_in;
			rpa.z = z;
			rpa.tx = tx;
			rpa.ty = ty;
			rpa.initial_x = initial_x;
			rpa.initial_y = initial_y;
			rpa.original_features = &original_features;
			rpa.unclipped_features = &unclipped_features;
			rpa.nextzoom = nextzoom;
			rpa.maxzoom = maxzoom;
			rpa.minzoom = minzoom;
			rpa.max_zoom_increment = max_zoom_increment;
			rpa.pass = pass;
			rpa.along = along;
			rpa.alongminus = alongminus;
			rpa.buffer = buffer;
			rpa.within = within.data();
			rpa.geomfile = geomfile;
			rpa.geompos = geompos;
			rpa.start_geompos = start_geompos.data();
			rpa.oprogress = &oprogress;
			rpa.todo = todo;
			rpa.fname = fname;
			rpa.child_shards = child_shards;
			rpa.prefilter_fp = prefilter_fp;
			rpa.layer_unmaps = layer_unmaps;
			rpa.global_stringpool = global_stringpool;
			rpa.pool_off = pool_off;
			rpa.filter = filter;
			rpa.unidecode_data = &unidecode_data;
			rpa.first_time = first_time;
			rpa.write_children = write_children;
			rpa.compressed = compressed_input;
			rpa.droprate = arg->droprate;

			// this does need to be a real thread, so we can pipe both to and from it
			if (pthread_create(&prefilter_writer, NULL, run_prefilter, &rpa) != 0) {
				perror("pthread_create (prefilter writer)");
				exit(EXIT_PTHREAD);
			}

			prefilter_read_fp = fdopen(prefilter_read, "r");
			if (prefilter_read_fp == NULL) {
				perror("fdopen prefilter output");
				exit(EXIT_OPEN);
			}
			prefilter_jp = json_begin_file(prefilter_read_fp);
		}

		// Read features, filter them, assign them to layers

		struct multiplier_state multiplier_state;
		bool drop_rest = false;		// are we dropping the remainder of a multiplier cluster whose first point was dropped?
		bool dropping_by_rate = false;	// are we dropping anything by rate in this tile, or keeping it only as part of a multiplier?

		next_feature_state next_feature_state;

		strategy strategy;
		strategy.detail_reduced = detail_reduced;

		for (size_t seq = 0;; seq++) {
			serial_feature sf;
			ssize_t which_serial_feature = -1;

			if (prefilter == NULL) {
				sf = next_feature(geoms, geompos_in, z, tx, ty, initial_x, initial_y, &original_features, &unclipped_features, nextzoom, maxzoom, minzoom, max_zoom_increment, pass, along, alongminus, buffer, within.data(), geomfile, geompos, start_geompos.data(), &oprogress, todo, fname, child_shards, filter, global_stringpool, pool_off, layer_unmaps, first_time, write_children, compressed_input, &multiplier_state, tile_stringpool, unidecode_data, next_feature_state, arg->droprate);
			} else {
				sf = parse_feature(prefilter_jp, z, tx, ty, layermaps, tiling_seg, layer_unmaps, postfilter != NULL, key_pool);
			}

			if (sf.t < 0) {
				break;
			}

			std::string &layername = (*layer_unmaps)[sf.segment][sf.layer];
			if (layers.count(layername) == 0) {
				layers.emplace(layername, layer_features());
			}
			struct layer_features &layer = layers.find(layername)->second;
			std::vector<std::shared_ptr<serial_feature>> &features = layer.features;

			if (sf.t == VT_POINT) {
				if (extent_previndex >= sf.index) {
					sf.extent = 1;
				} else {
					double radius = sqrt(sf.index - extent_previndex) / 4.0;
					sf.extent = M_PI * radius * radius;
					if (sf.extent < 1) {
						sf.extent = 1;
					}
				}

				extent_previndex = sf.index;
			}

			// Make label anchors early in tiling, even though it requires simplifying early,
			// so that if there is no label anchor for this feature in this tile,
			// we find out now rather than after we have already decided that there
			// are too_many_bytes.
			//
			// label anchors also need to happen before as-needed dropping and coalescing,
			// so that the geometry type matches for find_feature_to_accumulate_onto.
			// (or it could happen much later, after all the features are accumulated,
			// but then it would be too late for too_many_bytes)
			if (sf.t == VT_POLYGON && additional[A_GENERATE_POLYGON_LABEL_POINTS]) {
				// exclude features that are invisibly small at this zoom level
				if (line_is_too_small(sf.geometry, z, line_detail)) {
					continue;
				}
				if (sf.t == VT_POLYGON && get_mp_area(sf.geometry) <= 0) {
					continue;
				}

				drawvec ngeom = simplify_lines(sf.geometry, z, tx, ty, line_detail, !(prevent[P_CLIPPING] || prevent[P_DUPLICATION]), sf.simplification, sf.t == VT_POLYGON ? 4 : 0, shared_nodes, NULL, 0, "");
				if (ngeom.size() == 0) {
					continue;
				}
				sf.geometry = checkerboard_anchors(ngeom, tx, ty, z, sf.label_point);
				if (sf.geometry.size() == 0) {
					continue;
				}
				sf.t = VT_POINT;
			}

			unsigned long long drop_sequence = 0;
			if (additional[A_COALESCE_FRACTION_AS_NEEDED] || additional[A_DROP_FRACTION_AS_NEEDED] || prevent[P_DYNAMIC_DROP]) {
				drop_sequence = calculate_drop_sequence(sf);
			}

			if (sf.feature_minzoom > z + 1) {
				// if there is a feature whose first appearance is beyond the next zoom,
				// prevent stopping early at the next zoom
				dropping_by_rate = true;
			}

			if (sf.dropped == FEATURE_KEPT) {
				// this is a new multiplier cluster, so stop dropping features
				// that were dropped because the previous lead feature was dropped
				drop_rest = false;
			} else {
				can_stop_early = false;

				if (sf.dropped != FEATURE_DROPPED && sf.dropped != FEATURE_ADDED_FOR_MULTIPLIER_DENSITY) {
					// Does the current multiplier cluster already have too many features?
					// (Because we are dropping dynamically, and we have already filled the
					// cluster with features that were dynamically dropped from being
					// primary features)
					// If so, we have to drop this one, even if it would potentially qualify
					// as a secondary feature to be exposed by filtering
					if (layer.multiplier_cluster_size >= (size_t) retain_points_multiplier) {
						sf.dropped = FEATURE_DROPPED;
					}
				}
			}

			if (sf.dropped == FEATURE_DROPPED || drop_rest) {
				if (find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
					preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
					strategy.dropped_by_rate++;
					can_stop_early = false;
					continue;
				}
			}

			// only the first point of a multiplier cluster can be dropped
			// by any of these mechanisms. (but if one is, it drags the whole
			// cluster down with it by setting drop_rest).
			if (sf.dropped == FEATURE_KEPT) {
				if (gamma > 0) {
					if (manage_gap(sf.index, &previndex, scale, gamma, &gap) && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.dropped_by_gamma++;
						drop_rest = true;
						can_stop_early = false;
						continue;
					}
				}

				if (z <= cluster_maxzoom && cluster_distance != 0) {
					// This still uses merge_previndex instead of sf.gap
					// because the cluster size in -K is expecting to specify
					// distances between points that are subject to dot-dropping,
					// rather than wanting each feature to have a consistent
					// idea of density between zooms.
					if ((sf.index < merge_previndex || sf.index - merge_previndex < cluster_mingap) && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
						features[which_serial_feature]->clustered++;

						if (!additional[A_KEEP_POINT_CLUSTER_POSITION] &&
						    features[which_serial_feature]->t == VT_POINT &&
						    features[which_serial_feature]->geometry.size() == 1 &&
						    sf.geometry.size() == 1) {
							double x = (double) features[which_serial_feature]->geometry[0].x * features[which_serial_feature]->clustered;
							double y = (double) features[which_serial_feature]->geometry[0].y * features[which_serial_feature]->clustered;
							x += sf.geometry[0].x;
							y += sf.geometry[0].y;
							features[which_serial_feature]->geometry[0].x = x / (features[which_serial_feature]->clustered + 1);
							features[which_serial_feature]->geometry[0].y = y / (features[which_serial_feature]->clustered + 1);
						}

						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.coalesced_as_needed++;
						drop_rest = true;
						can_stop_early = false;
						continue;
					}
				} else if (additional[A_DROP_DENSEST_AS_NEEDED]) {
					add_sample_to(gaps, sf.gap, gaps_increment, seq);
					if (sf.gap < mingap) {
						can_stop_early = false;
						if (drop_feature_unless_it_can_be_added_to_a_multiplier_cluster(layer, sf, layer_unmaps, strategy, drop_rest, arg->attribute_accum, key_pool)) {
							continue;
						}
					}
				} else if (z <= cluster_maxzoom && (additional[A_CLUSTER_DENSEST_AS_NEEDED])) {
					// this is now just like coalesce-densest, except that instead of unioning the geometry,
					// it averages the point locations
					add_sample_to(gaps, sf.gap, gaps_increment, seq);
					if (sf.gap < mingap && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
						features[which_serial_feature]->clustered++;

						if (features[which_serial_feature]->t == VT_POINT &&
						    features[which_serial_feature]->geometry.size() == 1 &&
						    sf.geometry.size() == 1) {
							double x = (double) features[which_serial_feature]->geometry[0].x * features[which_serial_feature]->clustered;
							double y = (double) features[which_serial_feature]->geometry[0].y * features[which_serial_feature]->clustered;
							x += sf.geometry[0].x;
							y += sf.geometry[0].y;
							features[which_serial_feature]->geometry[0].x = x / (features[which_serial_feature]->clustered + 1);
							features[which_serial_feature]->geometry[0].y = y / (features[which_serial_feature]->clustered + 1);
						}

						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.coalesced_as_needed++;
						drop_rest = true;
						continue;
					}
				} else if (additional[A_COALESCE_DENSEST_AS_NEEDED]) {
					add_sample_to(gaps, sf.gap, gaps_increment, seq);
					if (sf.gap < mingap && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
						if (sf.t == VT_POINT || !line_is_too_small(sf.geometry, z, line_detail)) {
							coalesce_geometry(*features[which_serial_feature], sf);
						}
						features[which_serial_feature]->coalesced = true;
						coalesced_area += sf.extent;
						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.coalesced_as_needed++;
						drop_rest = true;
						can_stop_early = false;
						continue;
					}
				} else if (additional[A_DROP_SMALLEST_AS_NEEDED]) {
					add_sample_to(extents, sf.extent, extents_increment, seq);
					// search here is for LLONG_MAX, not minextent, because we are dropping features, not coalescing them,
					// so we shouldn't expect to find anything small that we can related this feature to.
					if (minextent != 0 && sf.extent + coalesced_area <= minextent) {
						can_stop_early = false;
						if (drop_feature_unless_it_can_be_added_to_a_multiplier_cluster(layer, sf, layer_unmaps, strategy, drop_rest, arg->attribute_accum, key_pool)) {
							continue;
						}
					}
				} else if (additional[A_COALESCE_SMALLEST_AS_NEEDED]) {
					add_sample_to(extents, sf.extent, extents_increment, seq);
					if (minextent != 0 && sf.extent + coalesced_area <= minextent && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, minextent)) {
						if (sf.t == VT_POINT || !line_is_too_small(sf.geometry, z, line_detail)) {
							coalesce_geometry(*features[which_serial_feature], sf);
						}
						features[which_serial_feature]->coalesced = true;
						coalesced_area += sf.extent;
						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.coalesced_as_needed++;
						drop_rest = true;
						can_stop_early = false;
						continue;
					}
				} else if (additional[A_DROP_FRACTION_AS_NEEDED] || prevent[P_DYNAMIC_DROP]) {
					add_sample_to(drop_sequences, drop_sequence, drop_sequences_increment, seq);
					if (mindrop_sequence != 0 && drop_sequence <= mindrop_sequence) {
						can_stop_early = false;
						if (drop_feature_unless_it_can_be_added_to_a_multiplier_cluster(layer, sf, layer_unmaps, strategy, drop_rest, arg->attribute_accum, key_pool)) {
							continue;
						}
					}
				} else if (additional[A_COALESCE_FRACTION_AS_NEEDED]) {
					add_sample_to(drop_sequences, drop_sequence, drop_sequences_increment, seq);
					if (mindrop_sequence != 0 && drop_sequence <= mindrop_sequence && find_feature_to_accumulate_onto(features, sf, which_serial_feature, layer_unmaps, LLONG_MAX)) {
						if (sf.t == VT_POINT || !line_is_too_small(sf.geometry, z, line_detail)) {
							coalesce_geometry(*features[which_serial_feature], sf);
						}
						features[which_serial_feature]->coalesced = true;
						preserve_attributes(arg->attribute_accum, sf, *features[which_serial_feature], key_pool);
						strategy.coalesced_as_needed++;
						drop_rest = true;
						can_stop_early = false;
						continue;
					}
				} else if (additional[A_DROP_BY_ATTRIBUTE_AS_NEEDED]) {
					mvt_value attr_val = find_attribute_value(&sf, *arg->drop_by_attribute_as_needed_attribute);
					double attr_numeric = 0;
					bool attr_valid = false;

					if (attr_val.type == mvt_double) {
						attr_numeric = attr_val.numeric_value.double_value;
						attr_valid = std::isfinite(attr_numeric);
					} else if (attr_val.type == mvt_float) {
						attr_numeric = attr_val.numeric_value.float_value;
						attr_valid = std::isfinite(attr_numeric);
					} else if (attr_val.type == mvt_int) {
						attr_numeric = attr_val.numeric_value.int_value;
						attr_valid = true;
					} else if (attr_val.type == mvt_uint) {
						attr_numeric = attr_val.numeric_value.uint_value;
						attr_valid = true;
					} else if (attr_val.type == mvt_sint) {
						attr_numeric = attr_val.numeric_value.sint_value;
						attr_valid = true;
					}

					if (attr_valid) {
						add_sample_to(attribute_values, attr_numeric, attribute_values_increment, seq);
						bool should_drop = arg->drop_by_attribute_descending
							? (minattribute != HUGE_VAL && attr_numeric > minattribute)
							: (minattribute != -HUGE_VAL && attr_numeric < minattribute);
						if (should_drop) {
							can_stop_early = false;
							if (drop_feature_unless_it_can_be_added_to_a_multiplier_cluster(layer, sf, layer_unmaps, strategy, drop_rest, arg->attribute_accum, key_pool)) {
								continue;
							}
						}
					}
				}
			}

			if (additional[A_CALCULATE_FEATURE_DENSITY]) {
				// Gamma is always 1 for this calculation so there is a reasonable
				// interpretation when no features are being dropped.
				// The spacing is only calculated if a feature would be retained by
				// that standard, so that duplicates aren't reported as infinitely dense.

				double o_density_previndex = density_previndex;
				if (!manage_gap(sf.index, &density_previndex, scale, 1, &density_gap)) {
					spacing = (sf.index - o_density_previndex) / scale;
				}
			}

			bool still_need_simplification_after_reduction = false;
			if (sf.t == VT_POLYGON) {
				bool simplified_away_by_reduction = false;

				bool prevent_tiny = prevent[P_TINY_POLYGON_REDUCTION] ||
						    (prevent[P_TINY_POLYGON_REDUCTION_AT_MAXZOOM] && z == maxzoom);
				if (!prevent_tiny && !additional[A_GRID_LOW_ZOOMS]) {
					sf.geometry = reduce_tiny_poly(sf.geometry, z, line_detail, &still_need_simplification_after_reduction, &simplified_away_by_reduction, &accum_area, tiny_polygon_size);
					if (simplified_away_by_reduction) {
						strategy.tiny_polygons++;
					}
					if (sf.geometry.size() == 0) {
						continue;
					}
				} else {
					still_need_simplification_after_reduction = true;  // reduction skipped, so always simplify
				}
			} else {
				still_need_simplification_after_reduction = true;  // not a polygon, so simplify
			}

			if (sf.t == VT_POLYGON || sf.t == VT_LINE) {
				if (line_is_too_small(sf.geometry, z, line_detail)) {
					continue;
				}
			}

			unsigned long long sfindex = sf.index;

			if (sf.geometry.size() > 0) {
				// There are two adjustments that we need to make
				// when determining if we have hit the feature count
				// or byte size limit:
				//
				// The max_tile_size and max_tile_features are inflated
				// to account for the number of multiplier clusters features
				// we are carrying around in addition to their lead features.

				size_t adjusted_max_tile_size = max_tile_size;
				if (lead_features_count > 0) {
					adjusted_max_tile_size = adjusted_max_tile_size * (lead_features_count + other_multiplier_cluster_features_count) / lead_features_count;
				}
				size_t adjusted_max_tile_features = max_tile_features;
				if (lead_features_count > 0) {
					adjusted_max_tile_features = adjusted_max_tile_features * (lead_features_count + other_multiplier_cluster_features_count) / lead_features_count;
				}

				// The number of features in the tile, meanwhile, is inflated
				// to account for the number of features that we have skipped over
				// because we were already over the limit, in addition to those
				// that we have actually added to the layer (as either lead or
				// multiplier features).

				size_t adjusted_feature_count = lead_features_count + other_multiplier_cluster_features_count;
				if (kept > 0) {
					adjusted_feature_count = adjusted_feature_count * (skipped + kept) / kept;
				}

				if (too_many_bytes || (adjusted_feature_count > adjusted_max_tile_size && !prevent[P_KILOBYTE_LIMIT])) {
					// Even being maximally conservative, each feature is still going to be
					// at least one byte in the output tile, so this can't possibly work.
					skipped++;
					too_many_bytes = true;
				} else if (too_many_features || ((adjusted_feature_count > adjusted_max_tile_features) && !prevent[P_FEATURE_LIMIT])) {
					skipped++;
					too_many_features = true;
				} else {
					kept++;

					if (features.size() == 0) {
						// the first feature of the the tile is always kept.
						// it may not have been marked kept in next_feature
						// if the previous feature was nominally the first
						// but has already been lost because its geometry was
						// clipped away
						sf.dropped = FEATURE_KEPT;
					}

					if (sf.dropped == FEATURE_KEPT && retain_points_multiplier > 1) {
						sf.full_keys.push_back(key_pool.pool("tippecanoe:retain_points_multiplier_first"));
						sf.full_values.emplace_back(mvt_bool, "true");
					}

					if (sf.dropped == FEATURE_KEPT) {
						layer.multiplier_cluster_size = 1;
						lead_features_count++;
					} else if (sf.dropped == FEATURE_ADDED_FOR_MULTIPLIER_DENSITY) {
						other_multiplier_cluster_features_count++;
					} else {
						layer.multiplier_cluster_size++;
						other_multiplier_cluster_features_count++;
					}

					for (auto &p : sf.edge_nodes) {
						shared_nodes.push_back(std::move(p));
					}

					sf.reduced = !still_need_simplification_after_reduction;
					sf.coalesced = false;
					sf.z = z;
					sf.tx = tx;
					sf.ty = ty;
					sf.line_detail = line_detail;
					sf.extra_detail = line_detail;
					sf.maxzoom = maxzoom;
					sf.spacing = spacing;
					sf.simplification = simplification;
					sf.renamed = -1;
					sf.clustered = 0;
					sf.tile_stringpool = tile_stringpool;

					if (trying_to_stop_early && line_detail == first_detail) {
						// only remove collinearities;
						// leave all other vertices for extreme overzooming
						sf.simplification = 0;
					}

					if (line_detail == detail && extra_detail >= 0 && z == maxzoom) {
						sf.extra_detail = extra_detail;
						// maximum allowed coordinate delta in geometries is 2^31 - 1
						// so we need to stay under that, including the buffer
						if (sf.extra_detail >= 30 - z) {
							sf.extra_detail = 30 - z;
						}
						tile_detail = sf.extra_detail;
					}

					features.push_back(std::make_shared<serial_feature>(sf));

					unsimplified_geometry_size += features.back()->geometry.size() * sizeof(draw);
					if (unsimplified_geometry_size > 10 * 1024 * 1024 && !additional[A_DETECT_SHARED_BORDERS]) {
						// we should be safe to simplify here with P_SIMPLIFY_SHARED_NODES, since they will
						// have been assembled globally, although that also means that simplification
						// may not be very effective for reducing memory usage.

						for (; simplified_geometry_through < features.size(); simplified_geometry_through++) {
							simplify_feature(&*features[simplified_geometry_through], shared_nodes, shared_nodes_map, nodepos, shared_nodes_bloom);

							if (features[simplified_geometry_through]->t == VT_POLYGON) {
								drawvec to_clean = features[simplified_geometry_through]->geometry;

								// don't scale up because this is still world coordinates
								coalesce_polygon(to_clean, false);
								features[simplified_geometry_through]->geometry = std::move(to_clean);
							}
						}

						unsimplified_geometry_size = 0;
					}
				}
			}

			merge_previndex = sfindex;
			coalesced_area = 0;
		}

		// We are done reading the features.
		// Close the prefilter if it was opened.
		// Close the output files for the next zoom level.

		if (prefilter != NULL) {
			json_end(prefilter_jp);
			if (fclose(prefilter_read_fp) != 0) {
				perror("close output from prefilter");
				exit(EXIT_CLOSE);
			}
			while (1) {
				int stat_loc;
				if (waitpid(prefilter_pid, &stat_loc, 0) < 0) {
					perror("waitpid for prefilter\n");
					exit(EXIT_PTHREAD);
				}
				if (WIFEXITED(stat_loc) || WIFSIGNALED(stat_loc)) {
					break;
				}
			}
			void *ret;
			if (pthread_join(prefilter_writer, &ret) != 0) {
				perror("pthread_join prefilter writer");
				exit(EXIT_PTHREAD);
			}
		}

		for (int j = 0; j < child_shards; j++) {
			if (within[j]) {
				long long estimated_complexity_out = geompos[j] - start_geompos[j];

				if (dropping_by_rate || next_feature_state.minzoom_feature_pending) {
					// large enough to make it not try to stop early
					estimated_complexity_out = 1LL << 32;
				}

				geomfile[j]->serialize_long_long(0, &geompos[j], fname);  // EOF
				geomfile[j]->end(&geompos[j], fname);
				within[j] = false;

				if (additional[A_VARIABLE_DEPTH_PYRAMID]) {
					fflush(geomfile[j]->fp);
					if (pwrite(fileno(geomfile[j]->fp), &estimated_complexity_out, sizeof(estimated_complexity_out), start_geompos[j]) != sizeof(estimated_complexity_out)) {
						perror("pwrite complexity");
						exit(EXIT_WRITE);
					}
				}
			}
		}

		first_time = false;

		// Adjust tile size limit based on the ratio of multiplier cluster features to lead features
		size_t adjusted_max_tile_size = max_tile_size;
		if (lead_features_count > 0) {
			adjusted_max_tile_size = adjusted_max_tile_size * (lead_features_count + other_multiplier_cluster_features_count) / lead_features_count;
		}
		size_t adjusted_max_tile_features = max_tile_features;
		if (lead_features_count > 0) {
			adjusted_max_tile_features = adjusted_max_tile_features * (lead_features_count + other_multiplier_cluster_features_count) / lead_features_count;
		}

		// Operations on the features within each layer:
		//
		// Tag features with their sequence within the layer, if required for --retain-points-multiplier
		// Add cluster size attributes to clustered features.
		// Update tilestats if attribute accumulation earlier introduced new values.
		// Detect shared borders.
		// Simplify geometries.
		// Reorder and coalesce.
		// Sort back into input order or by attribute value

		std::stable_sort(shared_nodes.begin(), shared_nodes.end());

		for (auto &kv : layers) {
			std::string const &layername = kv.first;
			std::vector<std::shared_ptr<serial_feature>> &features = kv.second.features;

			if (retain_points_multiplier > 1) {
				// mapping from input sequence to current sequence within this tile
				std::vector<std::pair<size_t, size_t>> feature_sequences;

				for (size_t i = 0; i < features.size(); i++) {
					feature_sequences.emplace_back(features[i]->seq, i);
				}

				// tag each feature with its sequence number within the layer
				// if the tile were sorted by input order
				//
				// these will be smaller numbers, and avoid the problem of the
				// original sequence number varying based on how many reader threads
				// there were reading the input
				std::stable_sort(feature_sequences.begin(), feature_sequences.end());
				for (size_t i = 0; i < feature_sequences.size(); i++) {
					size_t j = feature_sequences[i].second;
					serial_val sv(mvt_double, std::to_string(i));

					features[j]->full_keys.push_back(key_pool.pool("tippecanoe:retain_points_multiplier_sequence"));
					features[j]->full_values.push_back(sv);
				}
			}

			for (size_t i = 0; i < features.size(); i++) {
				serial_feature &p = *features[i];

				if (p.clustered > 0) {
					serial_val sv, sv2, sv3, sv4;
					long long point_count = p.clustered + 1;
					char abbrev[24];  // fits "%lld" of any long long, including the sign and the NUL

					p.full_keys.push_back(key_pool.pool("clustered"));
					sv.type = mvt_bool;
					sv.s = "true";
					p.full_values.push_back(sv);

					p.full_keys.push_back(key_pool.pool("point_count"));
					sv2.type = mvt_double;
					sv2.s = std::to_string(point_count);
					p.full_values.push_back(sv2);

					p.full_keys.push_back(key_pool.pool("sqrt_point_count"));
					sv3.type = mvt_double;
					sv3.s = std::to_string(round(100 * sqrt(point_count)) / 100.0);
					p.full_values.push_back(sv3);

					p.full_keys.push_back(key_pool.pool("point_count_abbreviated"));
					sv4.type = mvt_string;
					if (point_count >= 10000) {
						snprintf(abbrev, sizeof(abbrev), "%.0fk", point_count / 1000.0);
					} else if (point_count >= 1000) {
						snprintf(abbrev, sizeof(abbrev), "%.1fk", point_count / 1000.0);
					} else {
						snprintf(abbrev, sizeof(abbrev), "%lld", point_count);
					}
					sv4.s = abbrev;
					p.full_values.push_back(sv4);
				}

				for (size_t j = 0; j < p.full_keys.size(); j++) {
					// remove accumulation state
					size_t found = p.full_values[j].s.find('\0');
					if (found != std::string::npos) {
						p.full_values[j].s = p.full_values[j].s.substr(0, found);
					}
					add_tilestats(layername, z, layermaps, tiling_seg, layer_unmaps, *p.full_keys[j], p.full_values[j]);
				}
			}

			if (additional[A_DETECT_SHARED_BORDERS]) {
				find_common_edges(features, z, line_detail, simplification, maxzoom, merge_fraction);
			}

			int tasks = ceil((double) CPUS / *running);
			if (tasks < 1) {
				tasks = 1;
			}

			{
				std::vector<pthread_t> pthreads(tasks);
				std::vector<simplification_worker_arg> args;
				args.resize(tasks);
				for (int i = 0; i < tasks; i++) {
					args[i].task = i;
					args[i].tasks = tasks;
					args[i].features = &features;
					args[i].shared_nodes = &shared_nodes;
					args[i].shared_nodes_map = shared_nodes_map;
					args[i].nodepos = nodepos;
					args[i].shared_nodes_bloom = &shared_nodes_bloom;

					if (tasks > 1) {
						if (thread_create(&pthreads[i], NULL, simplification_worker, &args[i]) != 0) {
							perror("pthread_create");
							exit(EXIT_PTHREAD);
						}
					} else {
						simplification_worker(&args[i]);
					}
				}

				if (tasks > 1) {
					for (int i = 0; i < tasks; i++) {
						void *retval;

						if (pthread_join(pthreads[i], &retval) != 0) {
							perror("pthread_join");
						}
					}
				}
			}

			for (size_t i = 0; i < features.size(); i++) {
				signed char t = features[i]->t;

				{
					if (t == VT_POINT || draws_something(features[i]->geometry)) {
						// printf("segment %d layer %lld is %s\n", features[i].segment, features[i].layer, (*layer_unmaps)[features[i].segment][features[i].layer].c_str());

						features[i]->coalesced = false;
					}
				}
			}

			std::vector<std::shared_ptr<serial_feature>> &layer_features = features;

			if (additional[A_REORDER]) {
				std::stable_sort(layer_features.begin(), layer_features.end(), coalindexcmp_comparator());
			}

			if (additional[A_COALESCE]) {
				// coalesce adjacent identical features if requested

				size_t out = 0;
				if (layer_features.size() > 0) {
					out++;
				}

				for (size_t x = 1; x < layer_features.size(); x++) {
					size_t y = out - 1;

					if (out > 0 && coalcmp(&*layer_features[x], &*layer_features[y]) == 0) {
						for (size_t g = 0; g < layer_features[x]->geometry.size(); g++) {
							layer_features[y]->geometry.push_back(std::move(layer_features[x]->geometry[g]));
						}
						layer_features[y]->coalesced = true;
					} else {
						layer_features[out++] = layer_features[x];
					}
				}

				layer_features.resize(out);
			}

			{
				// clean up coalesced linestrings by simplification
				// and coalesced polygons by cleaning
				//
				// then close polygons

				size_t out = 0;

				for (size_t x = 0; x < layer_features.size(); x++) {
					if (layer_features[x]->coalesced && layer_features[x]->t == VT_LINE) {
						layer_features[x]->geometry = remove_noop(layer_features[x]->geometry, layer_features[x]->t, 0);
						if (!(prevent[P_SIMPLIFY] || (z == maxzoom && prevent[P_SIMPLIFY_LOW]))) {
							// XXX revisit: why does this not take zoom into account?
							layer_features[x]->geometry = simplify_lines(layer_features[x]->geometry, 32, 0, 0, 0,
												     !(prevent[P_CLIPPING] || prevent[P_DUPLICATION]), simplification, layer_features[x]->t == VT_POLYGON ? 4 : 0, shared_nodes, NULL, 0, "");
						}
					}

					if (layer_features[x]->t == VT_POLYGON) {
						if (layer_features[x]->coalesced) {
							// we can try scaling up because this is tile coordinates
							coalesce_polygon(layer_features[x]->geometry, true);
						}

						layer_features[x]->geometry = close_poly(layer_features[x]->geometry);
					}

					if (layer_features[x]->geometry.size() > 0) {
						layer_features[out++] = layer_features[x];
					}
				}

				layer_features.resize(out);
			}

			if (prevent[P_INPUT_ORDER]) {
				auto clustered = assemble_multiplier_clusters(layer_features);
				std::stable_sort(clustered.begin(), clustered.end(), preservecmp);
				layer_features = disassemble_multiplier_clusters(clustered);
			}

			if (order_by.size() != 0) {
				auto clustered = assemble_multiplier_clusters(layer_features);
				std::stable_sort(clustered.begin(), clustered.end(), ordercmp());
				layer_features = disassemble_multiplier_clusters(clustered);
			}

			if (z == maxzoom && limit_tile_feature_count_at_maxzoom != 0) {
				if (layer_features.size() > limit_tile_feature_count_at_maxzoom) {
					// this is maxzoom; ok to stop early still because they said to limit abruptly
					layer_features.resize(limit_tile_feature_count_at_maxzoom);
					too_many_features = false;  // don't try to drop; we have already truncated
					too_many_bytes = false;	    // don't try to drop; we have already truncated
					skipped = 0;		    // doesn't matter that we skipped features; we have truncated
				}
			} else if (limit_tile_feature_count != 0) {
				if (layer_features.size() > limit_tile_feature_count) {
					can_stop_early = false;
					layer_features.resize(limit_tile_feature_count);
					too_many_features = false;  // don't try to drop; we have already truncated
					too_many_bytes = false;	    // don't try to drop; we have already truncated
					skipped = 0;		    // doesn't matter that we skipped features; we have truncated
				}
			}
		}

		mvt_tile tile;
		size_t feature_count = 0;

		for (auto layer_iterator = layers.begin(); layer_iterator != layers.end(); ++layer_iterator) {
			std::vector<std::shared_ptr<serial_feature>> &layer_features = layer_iterator->second.features;
			feature_count += layer_features.size();

			mvt_layer layer;
			layer.name = layer_iterator->first;
			layer.version = 2;
			layer.extent = 1 << tile_detail;

			for (size_t x = 0; x < layer_features.size(); x++) {
				mvt_feature feature;

				if (layer_features[x]->t == VT_LINE || layer_features[x]->t == VT_POLYGON) {
					layer_features[x]->geometry = remove_noop(layer_features[x]->geometry, layer_features[x]->t, 0);
				}

				if (layer_features[x]->geometry.size() == 0) {
					layer_features[x] = std::make_shared<serial_feature>();
					continue;
				}

				feature.type = layer_features[x]->t;
				feature.geometry = to_feature(layer_features[x]->geometry);
				count += layer_features[x]->geometry.size();
				layer_features[x]->geometry.clear();

				feature.id = layer_features[x]->id;
				feature.has_id = layer_features[x]->has_id;

				decode_meta(*layer_features[x], layer, feature);
				for (size_t a = 0; a < layer_features[x]->full_keys.size(); a++) {
					serial_val sv = layer_features[x]->full_values[a];
					mvt_value v = stringified_to_mvt_value(sv.type, sv.s.c_str(), tile_stringpool);
					layer.tag(feature, *layer_features[x]->full_keys[a], v);
				}

				layer_features[x]->full_keys.clear();
				layer_features[x]->full_values.clear();

				if (additional[A_CALCULATE_FEATURE_DENSITY]) {
					int glow = 255;
					if (layer_features[x]->spacing > 0) {
						glow = (1 / layer_features[x]->spacing);
						if (glow > 255) {
							glow = 255;
						}
					}

					mvt_value v;
					v.type = mvt_sint;
					v.numeric_value.sint_value = glow;
					layer.tag(feature, "tippecanoe_feature_density", v);

					serial_val sv;
					sv.type = mvt_double;
					sv.s = std::to_string(glow);

					add_tilestats(layer.name, z, layermaps, tiling_seg, layer_unmaps, "tippecanoe_feature_density", sv);
				}

				layer.features.push_back(std::move(feature));
				layer_features[x] = std::make_shared<serial_feature>();
			}

			if (layer.features.size() > 0) {
				tile.layers.push_back(std::move(layer));
			}
		}

		if (postfilter != NULL) {
			tile.layers = filter_layers(postfilter, tile.layers, z, tx, ty, layermaps, tiling_seg, layer_unmaps, 1 << tile_detail);
		}

		if (z == 0 && unclipped_features < original_features / 2 && clipbboxes.size() == 0) {
			fprintf(stderr, "\n\nMore than half the features were clipped away at zoom level 0.\n");
			fprintf(stderr, "Is your data in the wrong projection? It should be in WGS84/EPSG:4326.\n");
		}

		size_t passes = pass + 1;
		double progress = floor(((((*geompos_in + *along - alongminus) / (double) todo) + pass) / passes + z) / (maxzoom + 1) * 1000) / 10;
		if (progress >= oprogress + 0.1) {
			if (!quiet && !quiet_progress && progress_time()) {
				fprintf(stderr, "  %3.1f%%  %d/%u/%u  \r", progress, z, tx, ty);
				fflush(stderr);
			}
			if (logger.json_enabled && progress_time()) {
				logger.progress_tile(progress);
			}
			oprogress = progress;
		}

		if (trying_to_stop_early && line_detail == first_detail && (!can_stop_early || next_feature_state.minzoom_feature_pending)) {
			// didn't work, try a lower detail
			continue;
		}

		// Again, adjust the retabulated feature count to estimate
		// how many total features there would have been if we hadn't
		// hit the limit and started dropping early.

		size_t adjusted_feature_count = feature_count;
		if (kept > 0) {
			adjusted_feature_count = adjusted_feature_count * (skipped + kept) / kept;
		}

		if (adjusted_feature_count > 0 && tile.layers.size() > 0) {
			if (too_many_features || (adjusted_feature_count > adjusted_max_tile_features && !prevent[P_FEATURE_LIMIT])) {
				if (adjusted_feature_count > arg->feature_count_out) {
					arg->feature_count_out = adjusted_feature_count;
				}

				if (!quiet) {
					fprintf(stderr, "tile %d/%u/%u has %zu (estimated %zu) features, >%zu    \n", z, tx, ty, feature_count, adjusted_feature_count, adjusted_max_tile_features);
				}

				if (trying_to_stop_early && line_detail == first_detail) {
					// didn't work, try a lower detail
					continue;
				}

				if (additional[A_INCREASE_GAMMA_AS_NEEDED] && gamma < 10) {
					if (gamma < 1) {
						gamma = 1;
					} else {
						gamma = gamma * 1.25;
					}

					if (gamma > arg->gamma_out) {
						arg->gamma_out = gamma;
						arg->still_dropping = true;
					}

					if (!quiet) {
						fprintf(stderr, "Going to try gamma of %0.3f to make it fit\n", gamma);
					}
					line_detail++;	// to keep it the same when the loop decrements it
					continue;
				} else if (mingap < ULONG_MAX && (additional[A_DROP_DENSEST_AS_NEEDED] || additional[A_COALESCE_DENSEST_AS_NEEDED] || additional[A_CLUSTER_DENSEST_AS_NEEDED])) {
					mingap_fraction = mingap_fraction * adjusted_max_tile_features / adjusted_feature_count * 0.80;
					if (mingap_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", mingap_fraction * 100.0);
						}
						mingap_fraction = 0.80;
					}
					unsigned long long m = choose_mingap(gaps, mingap_fraction, mingap);
					if (m > mingap) {
						mingap = m;
						if (mingap > arg->mingap_out) {
							arg->mingap_out = mingap;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping the sparsest %0.2f%% of the features to make it fit\n", mingap_fraction * 100.0);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase feature gap threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (additional[A_DROP_SMALLEST_AS_NEEDED] || additional[A_COALESCE_SMALLEST_AS_NEEDED]) {
					minextent_fraction = minextent_fraction * adjusted_max_tile_features / adjusted_feature_count * 0.75;
					if (minextent_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", minextent_fraction * 100.0);
						}
						minextent_fraction = 0.80;
					}
					long long m = choose_minextent(extents, minextent_fraction, minextent);
					if (m > minextent) {
						minextent = m;
						if (minextent > arg->minextent_out) {
							arg->minextent_out = minextent;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping the biggest %0.2f%% of the features to make it fit\n", minextent_fraction * 100.0);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase feature area threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (additional[A_DROP_BY_ATTRIBUTE_AS_NEEDED]) {
					minattribute_fraction = minattribute_fraction *
									adjusted_max_tile_features / adjusted_feature_count * 0.75;
					if (minattribute_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr,
								"Need to drop features, but calculated that we should keep %.1f%% of features\n",
								minattribute_fraction * 100.0);
						}
						minattribute_fraction = 0.80;
					}

					bool desc = arg->drop_by_attribute_descending;
					if (attribute_values.empty() && !quiet) {
						fprintf(stderr, "Warning: no features had a numeric value for attribute '%s'\n",
							arg->drop_by_attribute_as_needed_attribute->c_str());
					}
					double m = choose_minattribute(attribute_values, minattribute_fraction, minattribute, desc);
					bool better = desc ? m < minattribute : m > minattribute;
					if (better) {
						minattribute = m;
						bool propagate = desc ? minattribute < arg->minattribute_out : minattribute > arg->minattribute_out;
						if (propagate) {
							arg->minattribute_out = minattribute;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr,
								"Trying to keep features with '%s' %s %.6f to make it fit\n",
								arg->drop_by_attribute_as_needed_attribute->c_str(),
								desc ? "<=" : ">=",
								minattribute);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase attribute threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (feature_count > layers.size() && (additional[A_DROP_FRACTION_AS_NEEDED] || additional[A_COALESCE_FRACTION_AS_NEEDED] || prevent[P_DYNAMIC_DROP])) {
					// The 95% is a guess to avoid too many retries
					// and probably actually varies based on how much duplicated metadata there is

					mindrop_sequence_fraction = mindrop_sequence_fraction * adjusted_max_tile_features / adjusted_feature_count * 0.95;
					if (mindrop_sequence_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", mindrop_sequence_fraction * 100.0);
						}
						mindrop_sequence_fraction = 0.80;
					}
					unsigned long long m = choose_mindrop_sequence(drop_sequences, mindrop_sequence_fraction, mindrop_sequence);
					if (m > mindrop_sequence) {
						mindrop_sequence = m;
						if (mindrop_sequence > arg->mindrop_sequence_out) {
							if (!prevent[P_DYNAMIC_DROP]) {
								arg->mindrop_sequence_out = mindrop_sequence;
							}
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping %0.2f%% of the features to make it fit\n", mindrop_sequence_fraction * 100.0);
						}
						line_detail++;	// to keep it the same when the loop decrements it
						continue;
					} else {
						fprintf(stderr, "Can't increase feature count threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else {
					fprintf(stderr, "Try using --drop-fraction-as-needed or --drop-densest-as-needed.\n");
					return -1;
				}
			}

			std::string compressed;
			std::string pbf = tile.encode();

			tile.layers.clear();

			if (!prevent[P_TILE_COMPRESSION]) {
				compress(pbf, compressed, true);
			} else {
				compressed = pbf;
			}

			// And similarly, adjust the compressed byte size to estimate
			// what it would have been if we hadn't stopped dropping features early

			size_t adjusted_tile_size = compressed.size();
			if (kept > 0) {
				adjusted_tile_size = adjusted_tile_size * (kept + skipped) / kept;
			}

			if (too_many_bytes || (adjusted_tile_size > adjusted_max_tile_size && !prevent[P_KILOBYTE_LIMIT])) {
				if (adjusted_tile_size > arg->tile_size_out) {
					arg->tile_size_out = adjusted_tile_size;
				}

				if (!quiet) {
					if (adjusted_tile_size != compressed.size()) {
						fprintf(stderr, "tile %d/%u/%u size is %lld (probably really %zu) with detail %d, >%zu    \n", z, tx, ty, (long long) compressed.size(), adjusted_tile_size, line_detail, adjusted_max_tile_size);
					} else {
						fprintf(stderr, "tile %d/%u/%u size is %lld with detail %d, >%zu    \n", z, tx, ty, (long long) compressed.size(), line_detail, adjusted_max_tile_size);
					}
				}

				if (trying_to_stop_early && line_detail == first_detail) {
					// didn't work, try a lower detail
					continue;
				}

				if (additional[A_INCREASE_GAMMA_AS_NEEDED] && gamma < 10) {
					if (gamma < 1) {
						gamma = 1;
					} else {
						gamma = gamma * 1.25;
					}

					if (gamma > arg->gamma_out) {
						arg->gamma_out = gamma;
						arg->still_dropping = true;
					}

					if (!quiet) {
						fprintf(stderr, "Going to try gamma of %0.3f to make it fit\n", gamma);
					}
					line_detail++;	// to keep it the same when the loop decrements it
				} else if (mingap < ULONG_MAX && (additional[A_DROP_DENSEST_AS_NEEDED] || additional[A_COALESCE_DENSEST_AS_NEEDED] || additional[A_CLUSTER_DENSEST_AS_NEEDED])) {
					mingap_fraction = mingap_fraction * adjusted_max_tile_size / adjusted_tile_size * 0.80;
					if (mingap_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", mingap_fraction * 100.0);
						}
						mingap_fraction = 0.80;
					}
					unsigned long long m = choose_mingap(gaps, mingap_fraction, mingap);
					if (m > mingap) {
						mingap = m;
						if (mingap > arg->mingap_out) {
							arg->mingap_out = mingap;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping the sparsest %0.2f%% of the features to make it fit\n", mingap_fraction * 100.0);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase feature gap threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (additional[A_DROP_SMALLEST_AS_NEEDED] || additional[A_COALESCE_SMALLEST_AS_NEEDED]) {
					minextent_fraction = minextent_fraction * adjusted_max_tile_size / adjusted_tile_size * 0.75;
					if (minextent_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", minextent_fraction * 100.0);
						}
						minextent_fraction = 0.80;
					}
					long long m = choose_minextent(extents, minextent_fraction, minextent);
					if (m > minextent) {
						minextent = m;
						if (minextent > arg->minextent_out) {
							arg->minextent_out = minextent;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping the biggest %0.2f%% of the features to make it fit\n", minextent_fraction * 100.0);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase feature area threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (additional[A_DROP_BY_ATTRIBUTE_AS_NEEDED]) {
					minattribute_fraction = minattribute_fraction * adjusted_max_tile_size / adjusted_tile_size * 0.75;
					if (minattribute_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", minattribute_fraction * 100.0);
						}
						minattribute_fraction = 0.80;
					}
					bool desc2 = arg->drop_by_attribute_descending;
					if (attribute_values.empty() && !quiet) {
						fprintf(stderr, "Warning: no features had a numeric value for attribute '%s'\n",
							arg->drop_by_attribute_as_needed_attribute->c_str());
					}
					double m = choose_minattribute(attribute_values, minattribute_fraction, minattribute, desc2);
					bool better2 = desc2 ? m < minattribute : m > minattribute;
					if (better2) {
						minattribute = m;
						bool propagate2 = desc2 ? minattribute < arg->minattribute_out : minattribute > arg->minattribute_out;
						if (propagate2) {
							arg->minattribute_out = minattribute;
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping features with attribute '%s' %s %0.6f to make it fit\n", arg->drop_by_attribute_as_needed_attribute->c_str(), desc2 ? "<=" : ">=", minattribute);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase attribute threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else if (feature_count > layers.size() && (additional[A_DROP_FRACTION_AS_NEEDED] || additional[A_COALESCE_FRACTION_AS_NEEDED] || prevent[P_DYNAMIC_DROP])) {
					mindrop_sequence_fraction = mindrop_sequence_fraction * adjusted_max_tile_size / adjusted_tile_size * 0.75;
					if (mindrop_sequence_fraction > 0.80) {
						if (!quiet) {
							fprintf(stderr, "Need to drop features, but calculated that we should keep %.1f%% of features\n", mindrop_sequence_fraction * 100.0);
						}
						mindrop_sequence_fraction = 0.80;
					}
					unsigned long long m = choose_mindrop_sequence(drop_sequences, mindrop_sequence_fraction, mindrop_sequence);
					if (m > mindrop_sequence) {
						mindrop_sequence = m;
						if (mindrop_sequence > arg->mindrop_sequence_out) {
							if (!prevent[P_DYNAMIC_DROP]) {
								arg->mindrop_sequence_out = mindrop_sequence;
							}
							arg->still_dropping = true;
						}
						if (!quiet) {
							fprintf(stderr, "Going to try keeping %0.2f%% of the features to make it fit\n", mindrop_sequence_fraction * 100.0);
						}
						line_detail++;
						continue;
					} else {
						fprintf(stderr, "Can't increase feature count threshold further\n");
						exit(EXIT_INCOMPLETE);
					}
				} else {
					detail_reduced++;
				}
			} else {
				if (pthread_mutex_lock(&db_lock) != 0) {
					perror("pthread_mutex_lock");
					exit(EXIT_PTHREAD);
				}

				if (skipped > 0 || too_many_bytes || too_many_features) {
					fprintf(stderr, "Can't happen: writing tile even though we skipped\n");
					exit(EXIT_IMPOSSIBLE);
				}

				if (outdb != NULL) {
					mbtiles_write_tile(outdb, z, tx, ty, compressed.data(), compressed.size());
				} else if (outdir != NULL) {
					dir_write_tile(outdir, z, tx, ty, compressed);
				}

				if (pthread_mutex_unlock(&db_lock) != 0) {
					perror("pthread_mutex_unlock");
					exit(EXIT_PTHREAD);
				}

				if (trying_to_stop_early && line_detail == first_detail &&
				    !next_feature_state.minzoom_feature_pending) {
					// We succeeded in stopping early (and no excluded feature is
					// waiting for a deeper zoom). Prune the child tiles.

					strategy.truncated_zooms++;
					skip_children_out.insert(zxy(z, tx, ty));
				}

				strategy_out->add_from(strategy);
				return count;
			}
		} else {
			strategy_out->add_from(strategy);
			return count;
		}
	}

	fprintf(stderr, "could not make tile %d/%u/%u small enough\n", z, tx, ty);
	return -1;
}

void *run_thread(void *vargs) {
	write_tile_args *arg = (write_tile_args *) vargs;
	int *err_or_null = NULL;

	while (true) {
		bool done = false;

		if (pthread_mutex_lock(&task_lock) != 0) {
			perror("pthread_mutex_lock");
			exit(EXIT_PTHREAD);
		}

		struct task *task;
		if (arg->tasks->size() == 0) {
			done = true;
		} else {
			task = arg->tasks->back();
			arg->tasks->pop_back();
		}

		if (pthread_mutex_unlock(&task_lock) != 0) {
			perror("pthread_mutex_unlock");
			exit(EXIT_PTHREAD);
		}

		if (done) {
			break;
		}

		int j = task->fileno;

		if (arg->geomfd[j] < 0) {
			// only one source file for zoom level 0
			continue;
		}
		if (arg->geom_size[j] == 0) {
			continue;
		}

		// If this is zoom level 0, the geomfd will be uncompressed data,
		// because (at least for now) it needs to stay uncompressed during
		// the sort and post-sort maxzoom calculation and fixup so that
		// the sort can rearrange individual features and the fixup can
		// then adjust their minzooms without decompressing and recompressing
		// each feature.
		//
		// In higher zooms, it will be compressed data written out during the
		// previous zoom.

		FILE *geom = fdopen(arg->geomfd[j], "rb");
		if (geom == NULL) {
			perror("open geom");
			exit(EXIT_OPEN);
		}

		decompressor dc(geom);

		std::atomic<long long> geompos(0);
		long long prevgeom = 0;

		while (1) {
			int z;
			unsigned x, y;

			// These z/x/y are uncompressed so we can seek to the start of the
			// compressed feature data that immediately follows.

			long long estimated_complexity;
			if (dc.fread(&estimated_complexity, sizeof(estimated_complexity), 1, &geompos) != 1) {
				break;
			}
			if (!dc.deserialize_int(&z, &geompos)) {
				break;
			}
			dc.deserialize_uint(&x, &geompos);
			dc.deserialize_uint(&y, &geompos);
#if 0
// currently broken because also requires tracking nextzoom when skipping zooms
if (z != arg->zoom) {
fprintf(stderr, "Expected zoom %d, found zoom %d\n", arg->zoom, z);
exit(EXIT_IMPOSSIBLE);
}
#endif

			if (arg->compressed) {
				dc.begin();
			}

			long long len;

			struct zxy parent(z - 1, x / 2, y / 2);
			bool skip = false;

			// Normally a tile writes the geometry for its children on pass 0, and the
			// later passes of the same zoom, which are only retries with new thresholds,
			// must not write it again.
			bool write_children = (arg->pass == 0);

			if (arg->skip_children->count(parent) > 0) {
				if (dropping_features(arg->mingap, arg->minextent, arg->mindrop_sequence, arg->minattribute, arg->drop_by_attribute_descending)) {
					// The parent truncated its pyramid here, but the zoom has since had to
					// start dropping features, so the truncation no longer holds and this
					// tile has to be written after all. Its geometry is still in the stream.
					skip = false;

					// It was skipped on every earlier pass of this zoom, so its children
					// have never been written out. Write them on the first pass that
					// actually tiles it, rather than on pass 0 as usual. The thresholds
					// only ever increase within a zoom, so that is exactly the pass on
					// which this zoom started dropping.
					write_children = arg->first_dropping_pass;
				} else {
					// skip if the parent tile finished at the last zoom level and we aren't trying to drop
					skip = true;
				}
			} else {
				// do the tile if the parent didn't finish early
				skip = false;
			}

			if (skip) {
				skip_tile(&dc, &geompos, arg->compressed);
				len = 1;
			} else {
				arg->wrote_zoom = z;
				len = write_tile(&dc, &geompos, arg->global_stringpool, z, x, y, z == arg->maxzoom ? arg->full_detail : arg->low_detail, arg->min_detail, arg->outdb, arg->outdir, arg->buffer, arg->fname, arg->geomfile, arg->geompos, arg->minzoom, arg->maxzoom, arg->todo, arg->along, geompos, arg->gamma, arg->child_shards, arg->pool_off, arg->initial_x, arg->initial_y, arg->running, arg->simplification, arg->layermaps, arg->layer_unmaps, arg->tiling_seg, arg->pass, arg->mingap, arg->minextent, arg->mindrop_sequence, arg->minattribute, arg->prefilter, arg->postfilter, arg->filter, arg, arg->strategy, arg->compressed, arg->shared_nodes_map, arg->nodepos, *(arg->shared_nodes_bloom), (*arg->unidecode_data), estimated_complexity, write_children, arg->skip_children_out);
			}

			if (pthread_mutex_lock(&var_lock) != 0) {
				perror("pthread_mutex_lock");
				exit(EXIT_PTHREAD);
			}

			if (z == arg->maxzoom) {
				if (len > *arg->most) {
					*arg->midx = x;
					*arg->midy = y;
					*arg->most = len;
				} else if (len == *arg->most) {
					unsigned long long a = (((unsigned long long) x) << 32) | y;
					unsigned long long b = (((unsigned long long) *arg->midx) << 32) | *arg->midy;

					if (a < b) {
						*arg->midx = x;
						*arg->midy = y;
						*arg->most = len;
					}
				}
			}

			*arg->along += geompos - prevgeom;
			prevgeom = geompos;

			if (pthread_mutex_unlock(&var_lock) != 0) {
				perror("pthread_mutex_unlock");
				exit(EXIT_PTHREAD);
			}

			if (len < 0) {
				err_or_null = &arg->err;
				*err_or_null = z - 1;
				break;
			}
		}

		if (arg->pass == 1) {
			// Since the fclose() has closed the underlying file descriptor
			arg->geomfd[j] = -1;
		} else {
			int newfd = dup(arg->geomfd[j]);
			if (newfd < 0) {
				perror("dup geometry");
				exit(EXIT_OPEN);
			}
			if (lseek(newfd, 0, SEEK_SET) < 0) {
				perror("lseek geometry");
				exit(EXIT_SEEK);
			}
			arg->geomfd[j] = newfd;
		}

		if (fclose(geom) != 0) {
			perror("close geom");
			exit(EXIT_CLOSE);
		}
	}

	arg->running--;
	return err_or_null;
}

int traverse_zooms(int *geomfd, off_t *geom_size, char *global_stringpool, std::atomic<unsigned> *midx, std::atomic<unsigned> *midy, int &maxzoom, int minzoom, sqlite3 *outdb, const char *outdir, int buffer, const char *fname, const char *tmpdir, double gamma, int full_detail, int low_detail, int min_detail, long long *pool_off, unsigned *initial_x, unsigned *initial_y, double simplification, double maxzoom_simplification, std::vector<std::map<std::string, layermap_entry>> &layermaps, const char *prefilter, const char *postfilter, std::unordered_map<std::string, attribute_op> const *attribute_accum, json_object *filter, std::vector<strategy> &strategies, int iz, node *shared_nodes_map, size_t nodepos, std::string const &shared_nodes_bloom, int basezoom, double droprate, std::vector<std::string> const &unidecode_data, std::string const *drop_by_attribute_as_needed_attribute, bool drop_by_attribute_descending) {
	last_progress = 0;

	// The existing layermaps are one table per input thread.
	// We need to add another one per *tiling* thread so that it can be
	// safely changed during tiling.
	size_t layermaps_off = layermaps.size();
	for (size_t i = 0; i < CPUS; i++) {
		layermaps.emplace_back();
	}

	// Table to map segment and layer number back to layer name
	std::vector<std::vector<std::string>> layer_unmaps;
	for (size_t seg = 0; seg < layermaps.size(); seg++) {
		layer_unmaps.emplace_back();

		for (auto a = layermaps[seg].begin(); a != layermaps[seg].end(); ++a) {
			if (a->second.id >= layer_unmaps[seg].size()) {
				layer_unmaps[seg].resize(a->second.id + 1);
			}
			layer_unmaps[seg][a->second.id] = a->first;
		}
	}

	std::set<zxy> skip_children;

	int z;
	int largest_written = -1;

	for (z = iz; z <= maxzoom; z++) {
		std::atomic<long long> most(0);

		std::vector<compressor> compressors(TEMP_FILES);
		std::vector<compressor *> sub(TEMP_FILES);
		std::vector<std::atomic<long long> > subpos(TEMP_FILES);
		std::vector<int> subfd(TEMP_FILES);
		for (size_t j = 0; j < TEMP_FILES; j++) {
			std::string geomname = std::string(tmpdir) + "/geom" + std::to_string(j) + ".XXXXXXXX";
			subfd[j] = mkstemp_cloexec(&geomname[0]);
			// printf("%s\n", geomname.c_str());
			if (subfd[j] < 0) {
				perror(geomname.c_str());
				exit(EXIT_OPEN);
			}
			FILE *fp = fopen_oflag(geomname.c_str(), "wb", O_WRONLY | O_CLOEXEC);
			if (fp == NULL) {
				perror(geomname.c_str());
				exit(EXIT_OPEN);
			}
			compressors[j] = compressor(fp);
			sub[j] = &compressors[j];
			subpos[j] = 0;
			unlink(geomname.c_str());
		}

		size_t useful_threads = 0;
		long long todo = 0;
		for (size_t j = 0; j < TEMP_FILES; j++) {
			todo += geom_size[j];
			if (geom_size[j] > 0) {
				useful_threads++;
			}
		}

		size_t threads = CPUS;
		if (threads > TEMP_FILES / 4) {
			threads = TEMP_FILES / 4;
		}
		// XXX is it useful to divide further if we know we are skipping
		// some zoom levels? Is it faster to have fewer CPUs working on
		// sharding, but more deeply, or more CPUs, less deeply?
		if (threads > useful_threads) {
			threads = useful_threads;
		}

		// Round down to a power of 2
		for (int e = 0; e < 30; e++) {
			if (threads >= (1U << e) && threads < (1U << (e + 1))) {
				threads = 1U << e;
				break;
			}
		}
		if (threads >= (1U << 30)) {
			threads = 1U << 30;
		}
		if (threads < 1) {
			threads = 1;
		}

		// Assign temporary files to threads

		std::vector<task> tasks;
		tasks.resize(TEMP_FILES);

		std::vector<task *> dispatch;

		for (size_t j = 0; j < TEMP_FILES; j++) {
			if (geom_size[j] == 0) {
				continue;
			}

			tasks[j].fileno = j;
			tasks[j].todo = geom_size[j];
			dispatch.push_back(&tasks[j]);
		}

		std::sort(dispatch.begin(), dispatch.end());

		int err = INT_MAX;

		double zoom_gamma = gamma;
		unsigned long long zoom_mingap = 0;
		long long zoom_minextent = 0;
		unsigned long long zoom_mindrop_sequence = 0;
		double zoom_minattribute = drop_by_attribute_descending ? HUGE_VAL : -HUGE_VAL;
		size_t zoom_tile_size = 0;
		size_t zoom_feature_count = 0;
		std::set<zxy> skip_children_out;
		bool was_dropping = false;

		for (size_t pass = 0;; pass++) {
			// Tiles that were skipped because an ancestor truncated its pyramid are
			// revived on the pass where this zoom starts dropping features, and that
			// is the pass on which they have to write out their children.
			bool is_dropping = dropping_features(zoom_mingap, zoom_minextent, zoom_mindrop_sequence, zoom_minattribute, drop_by_attribute_descending);
			bool first_dropping_pass = is_dropping && !was_dropping;
			was_dropping = is_dropping;

			std::vector<pthread_t> pthreads(threads);
			std::vector<write_tile_args> args;
			args.resize(threads);
			std::atomic<int> running(threads);
			std::atomic<long long> along(0);
			atomic_strategy strategy;
			skip_children_out.clear();

			// must be recreate with each pass, since child threads consume it
			std::vector<task *> pass_dispatch = dispatch;

			for (size_t thread = 0; thread < threads; thread++) {
				args[thread].threadno = thread;
				args[thread].global_stringpool = global_stringpool;
				args[thread].min_detail = min_detail;
				args[thread].outdb = outdb;  // locked with db_lock
				args[thread].outdir = outdir;
				args[thread].buffer = buffer;
				args[thread].fname = fname;
				args[thread].geomfile = sub.data() + thread * (TEMP_FILES / threads);
				args[thread].geompos = subpos.data() + thread * (TEMP_FILES / threads);
				args[thread].todo = todo;
				args[thread].along = &along;  // locked with var_lock
				args[thread].gamma = zoom_gamma;
				args[thread].gamma_out = zoom_gamma;
				args[thread].mingap = zoom_mingap;
				args[thread].mingap_out = zoom_mingap;
				args[thread].minextent = zoom_minextent;
				args[thread].minextent_out = zoom_minextent;
				args[thread].mindrop_sequence = zoom_mindrop_sequence;
				args[thread].mindrop_sequence_out = zoom_mindrop_sequence;
				args[thread].minattribute = zoom_minattribute;
				args[thread].minattribute_out = zoom_minattribute;
				args[thread].drop_by_attribute_as_needed_attribute = drop_by_attribute_as_needed_attribute;
				args[thread].drop_by_attribute_descending = drop_by_attribute_descending;
				args[thread].tile_size_out = 0;
				args[thread].feature_count_out = 0;
				args[thread].child_shards = TEMP_FILES / threads;

				if (z == maxzoom && maxzoom_simplification > 0) {
					args[thread].simplification = maxzoom_simplification;
				} else {
					args[thread].simplification = simplification;
				}

				args[thread].geomfd = geomfd;
				args[thread].geom_size = geom_size;
				args[thread].midx = midx;  // locked with var_lock
				args[thread].midy = midy;  // locked with var_lock
				args[thread].maxzoom = maxzoom;
				args[thread].minzoom = minzoom;
				args[thread].basezoom = basezoom;
				args[thread].droprate = droprate;
				args[thread].full_detail = full_detail;
				args[thread].low_detail = low_detail;
				args[thread].most = &most;  // locked with var_lock
				args[thread].pool_off = pool_off;
				args[thread].initial_x = initial_x;
				args[thread].initial_y = initial_y;
				args[thread].layermaps = &layermaps;
				args[thread].layer_unmaps = &layer_unmaps;
				args[thread].tiling_seg = thread + layermaps_off;
				args[thread].prefilter = prefilter;
				args[thread].postfilter = postfilter;
				args[thread].attribute_accum = attribute_accum;
				args[thread].filter = filter;
				args[thread].unidecode_data = &unidecode_data;

				args[thread].tasks = &pass_dispatch;
				args[thread].running = &running;
				args[thread].pass = pass;
				args[thread].wrote_zoom = -1;
				args[thread].still_dropping = false;
				args[thread].strategy = &strategy;
				args[thread].zoom = z;
				args[thread].compressed = (z != iz);
				args[thread].shared_nodes_map = shared_nodes_map;
				args[thread].nodepos = nodepos;
				args[thread].shared_nodes_bloom = &shared_nodes_bloom;
				args[thread].skip_children = &skip_children;
				args[thread].skip_children_out.clear();
				args[thread].first_dropping_pass = first_dropping_pass;

				if (thread_create(&pthreads[thread], NULL, run_thread, &args[thread]) != 0) {
					perror("pthread_create");
					exit(EXIT_PTHREAD);
				}
			}

			bool again = false;
			bool extend_zooms = false;
			for (size_t thread = 0; thread < threads; thread++) {
				void *retval;

				if (pthread_join(pthreads[thread], &retval) != 0) {
					perror("pthread_join");
				}

				if (retval != NULL) {
					err = *((int *) retval);
				}

				for (auto const &zxy : args[thread].skip_children_out) {
					skip_children_out.insert(zxy);
				}

				if (args[thread].gamma_out > zoom_gamma) {
					zoom_gamma = args[thread].gamma_out;
					again = true;
				}
				if (args[thread].mingap_out > zoom_mingap) {
					zoom_mingap = args[thread].mingap_out;
					again = true;
				}
				if (args[thread].minextent_out > zoom_minextent) {
					zoom_minextent = args[thread].minextent_out;
					again = true;
				}
				if (args[thread].mindrop_sequence_out > zoom_mindrop_sequence) {
					zoom_mindrop_sequence = args[thread].mindrop_sequence_out;
					again = true;
				}
				bool attr_propagate = drop_by_attribute_descending
						? args[thread].minattribute_out < zoom_minattribute
						: args[thread].minattribute_out > zoom_minattribute;
				if (attr_propagate) {
					zoom_minattribute = args[thread].minattribute_out;
					again = true;
				}
				if (args[thread].tile_size_out > zoom_tile_size) {
					zoom_tile_size = args[thread].tile_size_out;
				}
				if (args[thread].feature_count_out > zoom_feature_count) {
					zoom_feature_count = args[thread].feature_count_out;
				}

				// Zoom counter might be lower than reality if zooms are being skipped
				if (args[thread].wrote_zoom > z) {
					z = args[thread].wrote_zoom;
				}
				if (args[thread].wrote_zoom > largest_written) {
					largest_written = args[thread].wrote_zoom;
				}

				if (args[thread].still_dropping) {
					extend_zooms = true;
				}
			}

			if (extend_zooms && (additional[A_EXTEND_ZOOMS] || extend_zooms_max > 0) && z == maxzoom && maxzoom < MAX_ZOOM) {
				maxzoom++;
				if (extend_zooms_max > 0) {
					extend_zooms_max--;
				}
			}

			if ((size_t) z >= strategies.size()) {
				strategies.resize(z + 1);
			}

			struct strategy s(strategy, zoom_tile_size, zoom_feature_count);
			strategies[z] = s;

			if (again) {
				if (outdb != NULL) {
					mbtiles_erase_zoom(outdb, z);
				} else if (outdir != NULL) {
					dir_erase_zoom(outdir, z);
				}
			} else {
				break;
			}
		}

		skip_children = std::move(skip_children_out);
		skip_children_out.clear();

		for (size_t j = 0; j < TEMP_FILES; j++) {
			// Can be < 0 if there is only one source file, at z0
			if (geomfd[j] >= 0) {
				if (close(geomfd[j]) != 0) {
					perror("close geom");
					exit(EXIT_CLOSE);
				}
			}
			if (sub[j]->fclose() != 0) {
				perror("close subfile");
				exit(EXIT_CLOSE);
			}

			struct stat geomst;
			if (fstat(subfd[j], &geomst) != 0) {
				perror("stat geom\n");
				exit(EXIT_STAT);
			}

			geomfd[j] = subfd[j];
			geom_size[j] = geomst.st_size;
		}

		if (err != INT_MAX) {
			return err;
		}
	}

	if (largest_written >= 0 && maxzoom > largest_written) {
		maxzoom = largest_written;
	}

	for (size_t j = 0; j < TEMP_FILES; j++) {
		// Can be < 0 if there is only one source file, at z0
		if (geomfd[j] >= 0) {
			if (close(geomfd[j]) != 0) {
				perror("close geom");
				exit(EXIT_CLOSE);
			}
		}
	}

	if (!quiet) {
		fprintf(stderr, "\n");
	}
	return maxzoom;
}

void atomic_strategy::add_from(struct strategy const &src) {
	dropped_by_rate += src.dropped_by_rate;
	dropped_by_gamma += src.dropped_by_gamma;
	dropped_as_needed += src.dropped_as_needed;
	coalesced_as_needed += src.coalesced_as_needed;
	detail_reduced += src.detail_reduced;
	tiny_polygons += src.tiny_polygons;
	truncated_zooms += src.truncated_zooms;
}