kopia lustrzana https://github.com/onthegomap/planetiler
405 wiersze
15 KiB
Java
405 wiersze
15 KiB
Java
package com.onthegomap.planetiler.archive;
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import static com.onthegomap.planetiler.util.Gzip.gzip;
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import static com.onthegomap.planetiler.worker.Worker.joinFutures;
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import com.onthegomap.planetiler.VectorTile;
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import com.onthegomap.planetiler.collection.FeatureGroup;
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import com.onthegomap.planetiler.config.PlanetilerConfig;
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import com.onthegomap.planetiler.geo.TileCoord;
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import com.onthegomap.planetiler.stats.Counter;
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import com.onthegomap.planetiler.stats.ProcessInfo;
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import com.onthegomap.planetiler.stats.ProgressLoggers;
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import com.onthegomap.planetiler.stats.Stats;
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import com.onthegomap.planetiler.stats.Timer;
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import com.onthegomap.planetiler.util.DiskBacked;
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import com.onthegomap.planetiler.util.Format;
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import com.onthegomap.planetiler.util.Hashing;
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import com.onthegomap.planetiler.util.LayerStats;
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import com.onthegomap.planetiler.worker.WorkQueue;
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import com.onthegomap.planetiler.worker.Worker;
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import com.onthegomap.planetiler.worker.WorkerPipeline;
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import java.io.IOException;
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import java.util.ArrayDeque;
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import java.util.ArrayList;
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import java.util.LinkedHashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.OptionalLong;
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import java.util.Queue;
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import java.util.concurrent.CompletableFuture;
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import java.util.concurrent.ExecutionException;
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import java.util.concurrent.atomic.AtomicReference;
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import java.util.concurrent.atomic.LongAccumulator;
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import java.util.function.Consumer;
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import java.util.function.LongSupplier;
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import java.util.stream.IntStream;
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import java.util.stream.Stream;
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import org.slf4j.Logger;
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import org.slf4j.LoggerFactory;
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/**
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* Final stage of the map generation process that encodes vector tiles using {@link VectorTile} and writes them to a
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* {@link WriteableTileArchive}.
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*/
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public class TileArchiveWriter {
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private static final Logger LOGGER = LoggerFactory.getLogger(TileArchiveWriter.class);
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private static final long MAX_FEATURES_PER_BATCH = 10_000;
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private static final long MAX_TILES_PER_BATCH = 1_000;
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private final Counter.Readable featuresProcessed;
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private final Counter memoizedTiles;
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private final WriteableTileArchive archive;
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private final PlanetilerConfig config;
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private final Stats stats;
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private final LayerStats layerStats;
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private final Counter.Readable[] tilesByZoom;
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private final Counter.Readable[] totalTileSizesByZoom;
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private final LongAccumulator[] maxTileSizesByZoom;
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private final Iterable<FeatureGroup.TileFeatures> inputTiles;
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private final AtomicReference<TileCoord> lastTileWritten = new AtomicReference<>();
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private final TileArchiveMetadata tileArchiveMetadata;
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private TileArchiveWriter(Iterable<FeatureGroup.TileFeatures> inputTiles, WriteableTileArchive archive,
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PlanetilerConfig config,
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TileArchiveMetadata tileArchiveMetadata, Stats stats, LayerStats layerStats) {
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this.inputTiles = inputTiles;
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this.archive = archive;
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this.config = config;
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this.tileArchiveMetadata = tileArchiveMetadata;
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this.stats = stats;
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this.layerStats = layerStats;
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tilesByZoom = IntStream.rangeClosed(0, config.maxzoom())
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.mapToObj(i -> Counter.newSingleThreadCounter())
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.toArray(Counter.Readable[]::new);
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totalTileSizesByZoom = IntStream.rangeClosed(0, config.maxzoom())
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.mapToObj(i -> Counter.newMultiThreadCounter())
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.toArray(Counter.Readable[]::new);
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maxTileSizesByZoom = IntStream.rangeClosed(0, config.maxzoom())
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.mapToObj(i -> new LongAccumulator(Long::max, 0))
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.toArray(LongAccumulator[]::new);
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memoizedTiles = stats.longCounter("archive_memoized_tiles");
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featuresProcessed = stats.longCounter("archive_features_processed");
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Map<String, LongSupplier> countsByZoom = new LinkedHashMap<>();
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for (int zoom = config.minzoom(); zoom <= config.maxzoom(); zoom++) {
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countsByZoom.put(Integer.toString(zoom), tilesByZoom[zoom]);
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}
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stats.counter("archive_tiles_written", "zoom", () -> countsByZoom);
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}
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/** Reads all {@code features}, encodes them in parallel, and writes to {@code output}. */
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public static void writeOutput(FeatureGroup features, WriteableTileArchive output, DiskBacked fileSize,
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TileArchiveMetadata tileArchiveMetadata, PlanetilerConfig config, Stats stats) {
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var timer = stats.startStage("archive");
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int readThreads = config.featureReadThreads();
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int threads = config.threads();
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int processThreads = threads < 10 ? threads : threads - readThreads;
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// when using more than 1 read thread: (N read threads) -> (1 merge thread) -> ...
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// when using 1 read thread we just have: (1 read & merge thread) -> ...
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Worker readWorker = null;
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Iterable<FeatureGroup.TileFeatures> inputTiles;
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String secondStageName;
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if (readThreads == 1) {
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secondStageName = "read";
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inputTiles = features;
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} else {
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secondStageName = "merge";
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var reader = features.parallelIterator(readThreads);
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inputTiles = reader.result();
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readWorker = reader.readWorker();
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}
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TileArchiveWriter writer = new TileArchiveWriter(inputTiles, output, config, tileArchiveMetadata, stats,
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features.layerStats());
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var pipeline = WorkerPipeline.start("archive", stats);
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// a larger tile queue size helps keep cores busy, but needs a lot of RAM
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// 5k works fine with 100GB of RAM, so adjust the queue size down from there
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// but no less than 100
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int queueSize = Math.max(
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100,
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(int) (5_000d * ProcessInfo.getMaxMemoryBytes() / 100_000_000_000d)
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);
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WorkerPipeline<TileBatch> encodeBranch, writeBranch = null;
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/*
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* To emit tiles in order, fork the input queue and send features to both the encoder and writer. The writer
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* waits on them to be encoded in the order they were received, and the encoder processes them in parallel.
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* One batch might take a long time to process, so make the queues very big to avoid idle encoding CPUs.
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*/
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WorkQueue<TileBatch> writerQueue = new WorkQueue<>("archive_writer_queue", queueSize, 1, stats);
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encodeBranch = pipeline
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.<TileBatch>fromGenerator(secondStageName, next -> {
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var writerEnqueuer = writerQueue.threadLocalWriter();
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writer.readFeaturesAndBatch(batch -> {
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next.accept(batch);
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writerEnqueuer.accept(batch); // also send immediately to writer
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});
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writerQueue.close();
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// use only 1 thread since readFeaturesAndBatch needs to be single-threaded
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}, 1)
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.addBuffer("reader_queue", queueSize)
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.sinkTo("encode", processThreads, writer::tileEncoderSink);
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// the tile writer will wait on the result of each batch to ensure tiles are written in order
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writeBranch = pipeline.readFromQueue(writerQueue)
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// use only 1 thread since tileWriter needs to be single-threaded
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.sinkTo("write", 1, writer::tileWriter);
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var loggers = ProgressLoggers.create()
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.addRatePercentCounter("features", features.numFeaturesWritten(), writer.featuresProcessed, true)
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.addFileSize(features)
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.addRateCounter("tiles", writer::tilesEmitted)
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.addFileSize(fileSize)
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.newLine()
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.addProcessStats()
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.newLine();
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if (readWorker != null) {
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loggers.addThreadPoolStats("read", readWorker);
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}
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loggers.addPipelineStats(encodeBranch)
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.addPipelineStats(writeBranch)
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.newLine()
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.add(writer::getLastTileLogDetails);
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var doneFuture = writeBranch == null ? encodeBranch.done() : joinFutures(writeBranch.done(), encodeBranch.done());
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loggers.awaitAndLog(doneFuture, config.logInterval());
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writer.printTileStats();
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timer.stop();
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}
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private String getLastTileLogDetails() {
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TileCoord lastTile = lastTileWritten.get();
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String blurb;
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if (lastTile == null) {
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blurb = "n/a";
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} else {
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blurb = "%d/%d/%d (z%d %s) %s".formatted(
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lastTile.z(), lastTile.x(), lastTile.y(),
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lastTile.z(),
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Format.defaultInstance().percent(archive.tileOrder().progressOnLevel(lastTile, config.bounds().tileExtents())),
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lastTile.getDebugUrl()
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);
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}
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return "last tile: " + blurb;
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}
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private void readFeaturesAndBatch(Consumer<TileBatch> next) {
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int currentZoom = Integer.MIN_VALUE;
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TileBatch batch = new TileBatch();
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long featuresInThisBatch = 0;
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long tilesInThisBatch = 0;
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for (var feature : inputTiles) {
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int z = feature.tileCoord().z();
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if (z != currentZoom) {
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LOGGER.trace("Starting z{}", z);
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currentZoom = z;
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}
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long thisTileFeatures = feature.getNumFeaturesToEmit();
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if (tilesInThisBatch > 0 &&
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(tilesInThisBatch >= MAX_TILES_PER_BATCH ||
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((featuresInThisBatch + thisTileFeatures) > MAX_FEATURES_PER_BATCH))) {
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next.accept(batch);
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batch = new TileBatch();
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featuresInThisBatch = 0;
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tilesInThisBatch = 0;
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}
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featuresInThisBatch += thisTileFeatures;
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tilesInThisBatch++;
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batch.in.add(feature);
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}
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if (!batch.in.isEmpty()) {
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next.accept(batch);
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}
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}
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private void tileEncoderSink(Iterable<TileBatch> prev) throws IOException {
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tileEncoder(prev, batch -> {
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// no next step
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});
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}
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private void tileEncoder(Iterable<TileBatch> prev, Consumer<TileBatch> next) throws IOException {
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/*
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* To optimize emitting many identical consecutive tiles (like large ocean areas), memoize output to avoid
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* recomputing if the input hasn't changed.
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*/
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byte[] lastBytes = null, lastEncoded = null;
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Long lastTileDataHash = null;
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boolean lastIsFill = false;
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boolean compactDb = config.compactDb();
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boolean skipFilled = config.skipFilledTiles();
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for (TileBatch batch : prev) {
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Queue<TileEncodingResult> result = new ArrayDeque<>(batch.size());
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FeatureGroup.TileFeatures last = null;
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// each batch contains tile ordered by z asc, x asc, y desc
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for (int i = 0; i < batch.in.size(); i++) {
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FeatureGroup.TileFeatures tileFeatures = batch.in.get(i);
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featuresProcessed.incBy(tileFeatures.getNumFeaturesProcessed());
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byte[] bytes, encoded;
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Long tileDataHash;
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if (tileFeatures.hasSameContents(last)) {
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bytes = lastBytes;
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encoded = lastEncoded;
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tileDataHash = lastTileDataHash;
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memoizedTiles.inc();
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} else {
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VectorTile en = tileFeatures.getVectorTileEncoder();
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if (skipFilled && (lastIsFill = en.containsOnlyFills())) {
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encoded = null;
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bytes = null;
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} else {
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encoded = en.encode();
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bytes = gzip(encoded);
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if (encoded.length > config.tileWarningSizeBytes()) {
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LOGGER.warn("{} {}kb uncompressed",
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tileFeatures.tileCoord(),
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encoded.length / 1024);
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}
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}
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lastEncoded = encoded;
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lastBytes = bytes;
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last = tileFeatures;
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if (compactDb && en.likelyToBeDuplicated() && bytes != null) {
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tileDataHash = generateContentHash(bytes);
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} else {
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tileDataHash = null;
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}
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lastTileDataHash = tileDataHash;
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}
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if (skipFilled && lastIsFill) {
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continue;
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}
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int zoom = tileFeatures.tileCoord().z();
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int encodedLength = encoded == null ? 0 : encoded.length;
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totalTileSizesByZoom[zoom].incBy(encodedLength);
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maxTileSizesByZoom[zoom].accumulate(encodedLength);
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result.add(
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new TileEncodingResult(tileFeatures.tileCoord(), bytes,
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tileDataHash == null ? OptionalLong.empty() : OptionalLong.of(tileDataHash))
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);
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}
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// hand result off to writer
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batch.out.complete(result);
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next.accept(batch);
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}
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}
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private void tileWriter(Iterable<TileBatch> tileBatches) throws ExecutionException, InterruptedException {
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archive.initialize(config, tileArchiveMetadata, layerStats);
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TileCoord lastTile = null;
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Timer time = null;
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int currentZ = Integer.MIN_VALUE;
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try (var tileWriter = archive.newTileWriter()) {
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for (TileBatch batch : tileBatches) {
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Queue<TileEncodingResult> encodedTiles = batch.out.get();
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TileEncodingResult encodedTile;
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while ((encodedTile = encodedTiles.poll()) != null) {
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TileCoord tileCoord = encodedTile.coord();
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assert lastTile == null || lastTile.compareTo(tileCoord) < 0 : "Tiles out of order %s before %s"
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.formatted(lastTile, tileCoord);
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lastTile = encodedTile.coord();
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int z = tileCoord.z();
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if (z != currentZ) {
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if (time == null) {
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LOGGER.info("Starting z{}", z);
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} else {
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LOGGER.info("Finished z{} in {}, now starting z{}", currentZ, time.stop(), z);
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}
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time = Timer.start();
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currentZ = z;
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}
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tileWriter.write(encodedTile);
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stats.wroteTile(z, encodedTile.tileData() == null ? 0 : encodedTile.tileData().length);
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tilesByZoom[z].inc();
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}
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lastTileWritten.set(lastTile);
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}
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tileWriter.printStats();
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}
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if (time != null) {
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LOGGER.info("Finished z{} in {}", currentZ, time.stop());
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}
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archive.finish(config);
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}
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private void printTileStats() {
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if (LOGGER.isDebugEnabled()) {
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Format format = Format.defaultInstance();
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LOGGER.debug("Tile stats:");
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long sumSize = 0;
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long sumCount = 0;
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long maxMax = 0;
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for (int z = config.minzoom(); z <= config.maxzoom(); z++) {
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long totalCount = tilesByZoom[z].get();
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long totalSize = totalTileSizesByZoom[z].get();
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sumSize += totalSize;
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sumCount += totalCount;
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long maxSize = maxTileSizesByZoom[z].get();
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maxMax = Math.max(maxMax, maxSize);
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LOGGER.debug("z{} avg:{} max:{}",
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z,
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format.storage(totalCount == 0 ? 0 : (totalSize / totalCount), false),
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format.storage(maxSize, false));
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}
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LOGGER.debug("all avg:{} max:{}",
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format.storage(sumCount == 0 ? 0 : (sumSize / sumCount), false),
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format.storage(maxMax, false));
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LOGGER.debug(" # features: {}", format.integer(featuresProcessed.get()));
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LOGGER.debug(" # tiles: {}", format.integer(this.tilesEmitted()));
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}
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}
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private long tilesEmitted() {
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return Stream.of(tilesByZoom).mapToLong(c -> c.get()).sum();
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}
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/**
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* Generates a hash over encoded and compressed tile.
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* <p>
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* Used as an optimization to avoid writing the same (mostly ocean) tiles over and over again.
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*/
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public static long generateContentHash(byte[] bytes) {
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return Hashing.fnv1a64(bytes);
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}
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/**
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* Container for a batch of tiles to be processed together in the encoder and writer threads.
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* <p>
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* The cost of encoding a tile may vary dramatically by its size (depending on the profile) so batches are sized
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* dynamically to put as little as 1 large tile, or as many as 10,000 small tiles in a batch to keep encoding threads
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* busy.
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*
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* @param in the tile data to encode
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* @param out the future that encoder thread completes to hand finished tile off to writer thread
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*/
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private record TileBatch(
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List<FeatureGroup.TileFeatures> in,
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CompletableFuture<Queue<TileEncodingResult>> out
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) {
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TileBatch() {
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this(new ArrayList<>(), new CompletableFuture<>());
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}
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public int size() {
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return in.size();
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}
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public boolean isEmpty() {
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return in.isEmpty();
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}
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}
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}
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