The sorry state of java deserialization

src: The sorry state of Java deserialization - 2024-09-22

I’ve been on a bit of a frustration-driven quest to solve a problem I frequently encounter working on the search engine, that is, reading data from disk.

You’d think this would be a pretty basic thing, but doing this in a way that is half-way performant is surprisingly hard and requires avoiding basically all the high level tools at your disposal.

There’s a common sentiment that modern hardware is fast, so this may not matter, but we aren’t speaking a 30% performance hit, the the question is how many orders of magnitude you’re willing to forego.

Indeed for dozens of megabytes of data this may still not matter too much, but when dealing with hundreds of gigabytes, it starts to chafe.

Let’s consider a simplified version of the 1 billion rows challenge. This is a very accurate representation of the sort of tasks you encounter doing search engine work, and simple enough to follow along.

As a reminder, the task is to read 1 billion temperature measurements, one assigned to a city, and then aggregate that with min/max/avg. The tests are run multiple times and we only care about the best run-time. This means that the data generally is read from RAM, but we’ll be happy to see disk-like performance.

Unlike the original challenge, we’ll not consider CSV ingestion, but let the contenders use whatever on-disk representations and loading mechanisms are available.

The benchmark will cover

Parquet
Protobuf
JDBC
Java’s stream and NIO APIs

Scroll to the end if you just want the numbers.

Smartphone enjoyers will want to switch to horizontal mode for this article due to code samples that barely fit on desktop

Reference numbers

Let’s also consider DuckDB as a sort of reference benchmark, as I will always shill for this fantastic tool. If we are orders of magnitude slower than DuckDB, something is off. We’ll store the numbers as fixed width integers and cities as varchars.

create table measurements(
   city varchar, 
   temperature int
)

Aggregating 1 billion rows in DuckDB from RAM takes about 2.6s, and storing it on disk uses 3.0 GB.

DuckDB also reads parquet files, and doing that increases the numbers to 4.5s, 3.8GB for Snappy and 5.5s, 3.0GB for Zstd. The intuition I want to provide here is that this is an opearation that could take seconds, and the data could sensibly fit in a few GB.

A decent hard drive will read sequential data at about 500 MB/s (as is the one we’re using for this test), so we’ll also add some reference times for 2GB and 10 GB assuming such a disk. Though the data is likely cached in RAM, we’ll still be happy if we approach these low-ball theoretical speeds.

Let’s look at our reference times before testing.

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB

DataInputStream + BufferedInputStream

Using InputStreams is a fairly idiomatic Java IO choice, even though this API is showing its age and is known not be the fastest option.

try (var citiesDis = new DataInputStream(
                         new BufferedInputStream(Files.newInputStream(citiesFile)));
     var temperaturesDis = new DataInputStream(
                         new BufferedInputStream(Files.newInputStream(temperaturesFile)))) 
{

    while (true) {
        int temperature = temperaturesDis.readShort();
        String city = citiesDis.readUTF();
        observers.computeIfAbsent(city, 
                         k -> new ResultObserver())
                      .observe(temperature / 100.);
    }
}
catch (EOFException ex) {} // ignore

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
DIS+BIS	300s	10 GB

Streaming the data over JDBC

A DBMS is often a popular choice for data storage, and they are indeed very good if your data is mutable. In this case it’s not, and the JDBC tax is steep. The Enterprise choice would of course be to put an an EntityManager on top of this, but I’m taking pity on the benchmark and foregoing that step.

This is querying DuckDB, but e.g. sqlite gives similar performance and a proper DBMS isn’t better. A big part of the problem isn’t with the databases, but the cost of moving the data from the database to Java.

try (var queryStmt = conn.createStatement();
     var rs = queryStmt.executeQuery(
         "select city, temperature from measurements"))
{
    queryStmt.setFetchSize(1000);
    while (rs.next()) {
        observerMap.computeIfAbsent(rs.getString(1), 
                       k -> new ResultsObserver())
                   .observe(rs.getInt(2) / 100.);
    }
}

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
DIS+BIS	300s	10 GB
Stream all 1B rows over JDBC	620s	3.0 GB

Protobuf

Protobuf is Google’s dog in the fight. It’s probably more widely known as a wire format, but can be used to store data on disk as well. It’s main benefit is the evolvable schema.

message OneBrcProtoRecord {
  string city = 1;
  int32 temperature = 2;
}

The suggested way of deserializing protobuf messages to disk is to use an input stream, and the parseDelimitedFrom-method. This is easy, but not very fast.

try (var is = new BufferedInputStream(
                    Files.newInputStream(tempFile))) {
    for (int i = 0; i < records; i++) {
        Onebrc.OneBrcProtoRecord record = 
               Onebrc.OneBrcProtoRecord.parseDelimitedFrom(is);
        stats.computeIfAbsent(record.getCity(), 
                  k -> new ResultsObserver())
             .observe(record.getTemperature());
    }
}

Note: The protobuf tutorial foregoes the step of adding the BufferedInputStream when performing this task. I won’t include it in the high score table but the runtime when you do that is in excess of half an hour.

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	7.5 GB
Stream all 1B rows over JDBC	620s	3.0 GB

Let’s instead use Java NIO to read the protobuf data. This is considerably more finicky, but also a fair bit faster. I’ll admit it’s possible I’m not doing this the optimal way, the documentation for this API is a bit thin.

ByteBuffer buffer = ByteBuffer.allocate(4096); // 4K block size to match the disk

try (var fc = (FileChannel) Files.newByteChannel(tempFile, StandardOpenOption.READ)) {

    for (int i = 0; i < records; i++) {

        int size;
        if (!buffer.hasRemaining()) {
            buffer.clear();
            fc.read(buffer);
            buffer.flip();
        }
        size = buffer.get();

        if (buffer.remaining() < size) {
            buffer.compact();
            fc.read(buffer);
            buffer.flip();
        }

        var cis = CodedInputStream.newInstance(
                     buffer.slice(buffer.position(), size));
        var record = Onebrc.OneBrcProtoRecord.parseFrom(cis);
        buffer.position(buffer.position() + size);

        stats.computeIfAbsent(record.getCity(), k -> new ResultsObserver())
             .observe(record.getTemperature());
    }

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
Protobuf + NIO	91s	12.3 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

Parquet in Java with parquet-floor shim

Reading parquet files in Java outside of the Hadoop ecosystem is a serious headache.

There’s a somewhat hacky shim available called parquet-floor that lets you access Hadoop’s parquet implementation outside of Hadoop. This may have a performance impact.

Hold onto your hat, there’s some boilerplate

try (var stream = ParquetReader.streamContent(tempFile.toFile(),
        HydratorSupplier.constantly(new OneBRCParquetHydrator()))) 
{
    stream.collect(Collectors.groupingBy(r -> r.city, 
                   Collectors.summarizingDouble(r -> r.temperature / 100.)))
            .forEach(...);
}

public static class OneBRCParquetRecord {
    public String city;
    public int temperature;

    public OneBRCParquetRecord() {}
    public OneBRCParquetRecord(String city, int temperature) {
        this.city = city;
        this.temperature = temperature;
    }

    public static MessageType schema = new MessageType(
            OneBRCParquetRecord.class.getSimpleName(),
            Types.required(BINARY).as(stringType()).named("city"),
            Types.required(INT32).named("temperature")
    );


    public void dehydrate(ValueWriter valueWriter) {
        valueWriter.write("city", city);
        valueWriter.write("temperature", temperature);
    }

    public OneBRCParquetRecord add(String heading, Object value) {
        if ("city".equals(heading)) {
            city = (String) value;
        } else if ("temperature".equals(heading)) {
            temperature = (Integer) value;
        } else {
            throw new UnsupportedOperationException("Unknown heading '" + heading + "'");
        }

        return this;
    }
}

static class OneBRCParquetHydrator implements Hydrator<OneBRCParquetRecord, OneBRCParquetRecord> {

    @Override
    public OneBRCParquetRecord start() {
        return new OneBRCParquetRecord();
    }

    @Override
    public OneBRCParquetRecord add(OneBRCParquetRecord target, String heading, Object value) {
        return target.add(heading, value);
    }

    @Override
    public OneBRCParquetRecord finish(OneBRCParquetRecord target) {
        return target;
    }

}

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
Protobuf + NIO	91s	12.3 GB
Parquet in Java	134s	2.4 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

Stockholm syndrome may have you thinking this is not that bad, but keep in mind we’re operating at 2% the speed of the reference timestamp for ~2 GB of data.

ObjectInputStream

Another contender from the dark ages of Java. I don’t think anyone is using this anymore…?

try (var ois = new ObjectInputStream(new BufferedInputStream(Files.newInputStream(data)))) {
    for (;;) {
        if (!(ois.readObject() instanceof JavaSerializableMeasurement jsm))
           continue;
		observerMap.computeIfAbsent(jsm.city, 
						k -> new ResultsObserver())
				   .observe(jsm.temperature / 100.);
	
    }
}
catch (EOFException e) {}

// ...

record JavaSerializableMeasurement(
  String city, 
  int temperature
) implements Serializable {}

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
Protobuf + NIO	91s	12.3 GB
Parquet in Java	134s	2.4 GB
OIS	260s	14 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

Custom solutions

Alright, this is kind of ridiculous. Let’s try to improve the numbers by implementing something ad-hoc.

First attempt is to just serialize the data ourselves to a file channel using a 4K buffer. The format will be

1 byte: city length
N bytes: city name
2 bytes: temperature

var buffer = ByteBuffer.allocate(4096);
try (var fc = 
	 (FileChannel) Files.newByteChannel(tempFile, 
                        StandardOpenOption.READ)) 
{

    buffer.flip();

    for (int i = 0; i < records; i++) {

        if (buffer.remaining() < 32) {
            buffer.compact();
            fc.read(buffer);
            buffer.flip();
        }

        int len = buffer.get();
        byte[] cityBytes = new byte[len];
        buffer.get(cityBytes);
        String city = new String(cityBytes);
        int temperature = buffer.getShort();

        stats.computeIfAbsent(city, k -> new ResultsObserver())
             .observe(temperature / 100.);
    }

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Disk I/O 10 GB	20.0s	10 GB
Custom Protocol	56s	9.5GB
Protobuf + NIO	91s	12.3 GB
Parquet in Java	134s	2.4 GB
OIS	260s	14 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

We can do much better though. The first attempt has two problems:

We’re allocating arrays every time we read an item
We’re parsing those bytes into Java UTF-8 Strings (which is slow)

Removing the string parsing shaves 15 seconds off the time, down to 40, but it’s not enough, it’s possible to entirely get rid of the allocations!

What if we put the city names in a separate newline-separated file, and just store their index in that file as a fixed-width ordinal?

This is in one way cheating a bit since we’re reading less data, but on the other hand I’ll argue it’s using your head when storing the data, and something a sane storage format should do.

List<String> cities = Files.readAllLines(citiesPath);

// using an array over a map saves about 6-7 seconds runtime
// via better locality
ResultsObserver[] stats = new ResultsObserver[cities.size()];
Arrays.setAll(stats, i -> new ResultsObserver());

try (var fc = (FileChannel) Files.newByteChannel(tempFile, StandardOpenOption.READ)) {

    for (int i = 0; i < records; i++) {

        if (buffer.remaining() < 3) {
            buffer.compact();
            fc.read(buffer);
            buffer.flip();
        }

        int cityOrd = buffer.get();
        int temperature = buffer.getShort();
        stats[cityOrd].observe(temperature / 100.);

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Custom Encoding 2	6.7s	2.8GB
Disk I/O 10 GB	20.0s	10 GB
Custom Encoding	56s	9.5GB
Protobuf + NIO	91s	12.3 GB
Parquet in Java	134s	2.4 GB
OIS	260s	14 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

We can squeeze some additional performance out of this by memory mapping the data, as mmap will give us some free readahead. We’ll also split it up into two different files to avoid unaligned short access.

// ...

try (var arena = Arena.ofConfined();
     var fcCities = (FileChannel) Files.newByteChannel(tempFile1, StandardOpenOption.READ);
     var fcMeasurements = (FileChannel) Files.newByteChannel(tempFile2, StandardOpenOption.READ)
) {
    var citiesSegment = 
              fcCities.map(FileChannel.MapMode.READ_ONLY, 
                           0, fcCities.size(), arena);
    var measurementsSegment = 
              fcMeasurements.map(FileChannel.MapMode.READ_ONLY, 
                                 0, fcMeasurements.size(), arena);


    for (int i = 0; i < records; i++) {
        int cityOrd 
            = citiesSegment.getAtIndex(ValueLayout.JAVA_BYTE, i);
        int temperature 
            = measurementsSegment.getAtIndex(ValueLayout.JAVA_SHORT, i);
            
        stats[cityOrd].observe(temperature / 100.);
    }

Impl	Runtime	Size On Disk
DuckDB	2.6s	3.0 GB
Disk I/O 2 GB	4.0s	2.0 GB
Parquet (Snappy) in DuckDB	4.5s	5.5 GB
Custom Encoding 2 + mmap	5.1s	2.8 GB
Parquet (Zstd) in DuckDB	5.5s	3.0 GB
Custom Encoding 2	6.7s	2.8 GB
Disk I/O 10 GB	20.0s	10 GB
Custom Encoding	56s	9.5GB
Protobuf + NIO	91s	12.3 GB
Parquet in Java	134s	2.4 GB
OIS	260s	14 GB
DIS+BIS	300s	10 GB
Protobuf + BIS	580s	12.3 GB
Stream all 1B rows over JDBC	620s	3.0 GB

I admit I don’t understand these results. There’s clearly nothing in the runtime itself that prevents these types of speeds. The supposedly good solutions are an order of magnitude slower than they should be. It’s relatively trivial to build something that’s faster, so why isn’t the code fast?

Disk I/O is a bottleneck on a good day. We don’t need to make it 10 times slower.

Per popular request, a repo is up with most of these benchmarks: https://github.com/vlofgren/Serialization1BRCBenchmark/. This blogpost sat as a nearly finished draft for almost a month during which I messed with the framework I created to run these benchmarks, so it’s not the exact same code, but the results are similar enough.

🗒️ l-lin

Explorer

The sorry state of java deserialization

Reference numbers

DataInputStream + BufferedInputStream

Streaming the data over JDBC

Protobuf

Parquet in Java with parquet-floor shim

ObjectInputStream

Custom solutions

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