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Kafka: Integrating Engine tables with Kafka
/**
* Copyright (c) 2016-2022 Deephaven Data Labs and Patent Pending
*/
package io.deephaven.kafka.publish;
import io.deephaven.api.ColumnName;
import io.deephaven.base.verify.Assert;
import io.deephaven.chunk.IntChunk;
import io.deephaven.chunk.LongChunk;
import io.deephaven.chunk.ObjectChunk;
import io.deephaven.configuration.Configuration;
import io.deephaven.engine.liveness.LivenessArtifact;
import io.deephaven.engine.liveness.LivenessScope;
import io.deephaven.engine.rowset.RowSequence;
import io.deephaven.engine.rowset.RowSet;
import io.deephaven.engine.table.ChunkSource;
import io.deephaven.engine.table.ColumnSource;
import io.deephaven.engine.table.ModifiedColumnSet;
import io.deephaven.engine.table.Table;
import io.deephaven.engine.table.TableUpdate;
import io.deephaven.engine.table.impl.BlinkTableTools;
import io.deephaven.engine.table.impl.InstrumentedTableUpdateListenerAdapter;
import io.deephaven.engine.table.impl.QueryTable;
import io.deephaven.engine.table.impl.sources.ReinterpretUtils;
import io.deephaven.engine.updategraph.UpdateGraph;
import io.deephaven.kafka.KafkaPublishOptions;
import io.deephaven.util.QueryConstants;
import io.deephaven.util.SafeCloseable;
import io.deephaven.util.annotations.InternalUseOnly;
import io.deephaven.util.annotations.ReferentialIntegrity;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.apache.kafka.common.serialization.Serializer;
import org.jetbrains.annotations.NotNull;
import java.time.Instant;
import java.util.Objects;
import java.util.Properties;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
/**
* This class is an internal implementation detail for io.deephaven.kafka; is not intended to be used directly by client
* code. It lives in a separate package as a means of code organization.
*/
@InternalUseOnly
public class PublishToKafka extends LivenessArtifact {
public static final int CHUNK_SIZE =
Configuration.getInstance().getIntegerForClassWithDefault(PublishToKafka.class, "chunkSize", 2048);
private final Table table;
private final KafkaProducer producer;
private final String defaultTopic;
private final Integer defaultPartition;
private final KeyOrValueSerializer keyChunkSerializer;
private final KeyOrValueSerializer valueChunkSerializer;
private final ColumnSource topicColumnSource;
private final ColumnSource partitionColumnSource;
private final ColumnSource timestampColumnSource;
@ReferentialIntegrity
private final PublishListener publishListener;
/**
* @deprecated please use {@link io.deephaven.kafka.KafkaTools#produceFromTable(KafkaPublishOptions)}
*/
@Deprecated(forRemoval = true)
public PublishToKafka(
final Properties props,
final Table table,
final String topic,
final String[] keyColumns,
final Serializer kafkaKeySerializer,
final KeyOrValueSerializer keyChunkSerializer,
final String[] valueColumns,
final Serializer kafkaValueSerializer,
final KeyOrValueSerializer valueChunkSerializer,
final boolean publishInitial) {
this(props, table, topic, null, keyColumns, kafkaKeySerializer, keyChunkSerializer, valueColumns,
kafkaValueSerializer, valueChunkSerializer, null, null, null, publishInitial);
}
/**
*
* Construct a publisher for {@code table} according the to Kafka {@code props} for the supplied {@code topic}.
*
* The new publisher will produce records for existing {@code table} data at construction.
*
* If {@code table} is a dynamic, refreshing table ({@link Table#isRefreshing()}), the calling thread must block the
* {@link UpdateGraph update graph} by holding either its {@link UpdateGraph#exclusiveLock() exclusive lock} or its
* {@link UpdateGraph#sharedLock() shared lock}. The publisher will install a listener in order to produce new
* records as updates become available. Callers must be sure to maintain a reference to the publisher and ensure
* that it remains {@link io.deephaven.engine.liveness.LivenessReferent live}. The easiest way to do this may be to
* construct the publisher enclosed by a {@link io.deephaven.engine.liveness.LivenessScope liveness scope} with
* {@code enforceStrongReachability} specified as {@code true}, and {@link LivenessScope#release() release} the
* scope when publication is no longer needed. For example:
*
*
* // To initiate publication:
* final LivenessScope publisherScope = new LivenessScope(true);
* try (final SafeCloseable ignored = LivenessScopeStack.open(publisherScope, false)) {
* new PublishToKafka(...);
* }
* // To cease publication:
* publisherScope.release();
*
*
* @param props The Kafka {@link Properties}
* @param table The source {@link Table}
* @param defaultTopic The default destination topic
* @param defaultPartition The default destination partition
* @param keyColumns Optional array of string column names from table for the columns corresponding to Kafka's Key
* field.
* @param kafkaKeySerializer The kafka {@link Serializer} to use for keys
* @param keyChunkSerializer Optional {@link KeyOrValueSerializer} to consume table data and produce Kafka record
* keys in chunk-oriented fashion
* @param valueColumns Optional array of string column names from table for the columns corresponding to Kafka's
* Value field.
* @param kafkaValueSerializer The kafka {@link Serializer} to use for values
* @param valueChunkSerializer Optional {@link KeyOrValueSerializer} to consume table data and produce Kafka record
* values in chunk-oriented fashion
* @param publishInitial If the initial data in {@code table} should be published
* @param topicColumn The topic column. When set, uses the the given {@link CharSequence} column from {@code table}
* as the first source for setting the kafka record topic.
* @param partitionColumn The partition column. When set, uses the the given {@code int} column from {@code table}
* as the first source for setting the kafka record partition.
* @param timestampColumn The timestamp column. When set, uses the the given {@link Instant} column from
* {@code table} as the first source for setting the kafka record timestamp.
*/
public PublishToKafka(
final Properties props,
Table table,
final String defaultTopic,
final Integer defaultPartition,
final String[] keyColumns,
final Serializer kafkaKeySerializer,
final KeyOrValueSerializer keyChunkSerializer,
final String[] valueColumns,
final Serializer kafkaValueSerializer,
final KeyOrValueSerializer valueChunkSerializer,
final ColumnName topicColumn,
final ColumnName partitionColumn,
final ColumnName timestampColumn,
final boolean publishInitial) {
this.table = (table = table.coalesce());
this.producer = new KafkaProducer<>(
props,
Objects.requireNonNull(kafkaKeySerializer),
Objects.requireNonNull(kafkaValueSerializer));
this.defaultTopic = defaultTopic;
this.defaultPartition = defaultPartition;
this.keyChunkSerializer = keyChunkSerializer;
this.valueChunkSerializer = valueChunkSerializer;
this.topicColumnSource = topicColumn == null
? null
: table.getColumnSource(topicColumn.name(), CharSequence.class);
this.partitionColumnSource = partitionColumn == null
? null
: table.getColumnSource(partitionColumn.name(), int.class);
this.timestampColumnSource = timestampColumn == null
? null
: ReinterpretUtils.instantToLongSource(table.getColumnSource(timestampColumn.name(), Instant.class));
if (publishInitial) {
// Publish the initial table state
try (final PublicationGuard guard = new PublicationGuard()) {
publishMessages(table.getRowSet(), false, true, guard);
}
}
// Install a listener to publish subsequent updates
if (table.isRefreshing()) {
table.addUpdateListener(publishListener = new PublishListener(
getModifiedColumnSet(table, keyColumns),
getModifiedColumnSet(table, valueColumns)));
manage(publishListener);
} else {
publishListener = null;
producer.close();
}
}
private static ModifiedColumnSet getModifiedColumnSet(@NotNull final Table table, final String[] columns) {
return (columns == null)
? ModifiedColumnSet.EMPTY
: ((QueryTable) table).newModifiedColumnSet(columns);
}
private String topic(ObjectChunk topicChunk, int index) {
if (topicChunk == null) {
return defaultTopic;
}
final CharSequence charSequence = topicChunk.get(index);
return charSequence == null ? defaultTopic : charSequence.toString();
}
private Integer partition(IntChunk partitionChunk, int index) {
if (partitionChunk == null) {
return defaultPartition;
}
final int partition = partitionChunk.get(index);
return partition == QueryConstants.NULL_INT ? defaultPartition : Integer.valueOf(partition);
}
public static Long timestampMillis(LongChunk nanosChunk, int index) {
if (nanosChunk == null) {
return null;
}
final long nanos = nanosChunk.get(index);
return nanos == QueryConstants.NULL_LONG ? null : TimeUnit.NANOSECONDS.toMillis(nanos);
}
private static T object(ObjectChunk chunk, int index) {
return chunk == null ? null : chunk.get(index);
}
private static ChunkSource.GetContext makeGetContext(ColumnSource source, int chunkSize) {
return source == null ? null : source.makeGetContext(chunkSize);
}
private void publishMessages(@NotNull final RowSet rowsToPublish, final boolean usePrevious,
final boolean publishValues, @NotNull final PublicationGuard guard) {
if (rowsToPublish.isEmpty()) {
return;
}
guard.onSend(rowsToPublish.size());
final int chunkSize = (int) Math.min(CHUNK_SIZE, rowsToPublish.size());
try (final RowSequence.Iterator rowsIterator = rowsToPublish.getRowSequenceIterator();
final KeyOrValueSerializer.Context keyContext = keyChunkSerializer != null
? keyChunkSerializer.makeContext(chunkSize)
: null;
final KeyOrValueSerializer.Context valueContext = publishValues && valueChunkSerializer != null
? valueChunkSerializer.makeContext(chunkSize)
: null;
final ChunkSource.GetContext topicContext = makeGetContext(topicColumnSource, chunkSize);
final ChunkSource.GetContext partitionContext = makeGetContext(partitionColumnSource, chunkSize);
final ChunkSource.GetContext timestampContext = makeGetContext(timestampColumnSource, chunkSize)) {
while (rowsIterator.hasMore()) {
final RowSequence chunkRowKeys = rowsIterator.getNextRowSequenceWithLength(chunkSize);
final ObjectChunk keyChunk = keyContext == null
? null
: keyChunkSerializer.handleChunk(keyContext, chunkRowKeys, usePrevious);
final ObjectChunk valueChunk = valueContext == null
? null
: valueChunkSerializer.handleChunk(valueContext, chunkRowKeys, usePrevious);
final ObjectChunk topicChunk = topicContext == null
? null
: (usePrevious
? topicColumnSource.getPrevChunk(topicContext, chunkRowKeys)
: topicColumnSource.getChunk(topicContext, chunkRowKeys))
.asObjectChunk();
final IntChunk partitionChunk = partitionContext == null
? null
: (usePrevious
? partitionColumnSource.getPrevChunk(partitionContext, chunkRowKeys)
: partitionColumnSource.getChunk(partitionContext, chunkRowKeys))
.asIntChunk();
final LongChunk timestampChunk = timestampContext == null
? null
: (usePrevious
? timestampColumnSource.getPrevChunk(timestampContext, chunkRowKeys)
: timestampColumnSource.getChunk(timestampContext, chunkRowKeys))
.asLongChunk();
final int numRecords = chunkRowKeys.intSize();
for (int ii = 0; ii < numRecords; ++ii) {
final ProducerRecord record = new ProducerRecord<>(
topic(topicChunk, ii),
partition(partitionChunk, ii),
timestampMillis(timestampChunk, ii),
object(keyChunk, ii),
object(valueChunk, ii));
producer.send(record, guard);
}
}
}
}
/**
* Re-usable, {@link SafeCloseable} {@link Callback} used to bracket multiple calls to
* {@link KafkaProducer#send(ProducerRecord, Callback) send} and ensure correct completion. Used in the following
* pattern:
*
*
* final PublicationGuard guard = new PublicationGuard();
* try (final Closeable ignored = guard) {
* // Call producer.send(record, guard) 0 or more times
* }
*
*/
private class PublicationGuard implements Callback, SafeCloseable {
private final AtomicLong sentCount = new AtomicLong();
private final AtomicLong completedCount = new AtomicLong();
private final AtomicReference sendException = new AtomicReference<>();
private volatile boolean closed;
private void reset() {
sentCount.set(0);
completedCount.set(0);
sendException.set(null);
closed = false;
}
private void onSend(final long messagesToSend) {
if (closed) {
throw new IllegalStateException("Tried to send using a guard that is no longer open");
}
sentCount.addAndGet(messagesToSend);
}
@Override
public void onCompletion(@NotNull final RecordMetadata metadata, final Exception exception) {
completedCount.getAndIncrement();
if (exception != null) {
sendException.compareAndSet(null, exception);
}
}
@Override
public void close() {
closed = true;
try {
final long localSentCount = sentCount.get();
if (localSentCount == 0) {
return;
}
try {
producer.flush();
} catch (Exception e) {
throw new KafkaPublisherException("KafkaProducer reported flush failure", e);
}
final Exception localSendException = sendException.get();
if (localSendException != null) {
throw new KafkaPublisherException("KafkaProducer reported send failure", localSendException);
}
final long localCompletedCount = completedCount.get();
if (localSentCount != localCompletedCount) {
throw new KafkaPublisherException(String.format("Sent count %d does not match completed count %d",
localSentCount, localCompletedCount));
}
} finally {
reset();
}
}
}
private class PublishListener extends InstrumentedTableUpdateListenerAdapter {
private final ModifiedColumnSet keysModified;
private final ModifiedColumnSet valuesModified;
private final boolean isBlink;
private final PublicationGuard guard = new PublicationGuard();
private PublishListener(
@NotNull final ModifiedColumnSet keysModified,
@NotNull final ModifiedColumnSet valuesModified) {
super("PublishToKafka", table, false);
this.keysModified = keysModified;
this.valuesModified = valuesModified;
this.isBlink = BlinkTableTools.isBlink(table);
}
@Override
public void onUpdate(TableUpdate upstream) {
Assert.assertion(!keysModified.containsAny(upstream.modifiedColumnSet()),
"!keysModified.containsAny(upstream.modifiedColumnSet())", "Key columns should never be modified");
try (final SafeCloseable ignored = guard) {
if (isBlink) {
Assert.assertion(upstream.modified().isEmpty(), "upstream.modified.empty()");
Assert.assertion(upstream.shifted().empty(), "upstream.shifted.empty()");
// We always ignore removes on streams, and expect no modifies or shifts
publishMessages(upstream.added(), false, true, guard);
return;
}
// Regular table, either keyless, add-only, or aggregated
publishMessages(upstream.removed(), true, false, guard);
if (valuesModified.containsAny(upstream.modifiedColumnSet())) {
try (final RowSet addedAndModified = upstream.added().union(upstream.modified())) {
publishMessages(addedAndModified, false, true, guard);
}
} else {
publishMessages(upstream.added(), false, true, guard);
}
}
}
}
@Override
protected void destroy() {
super.destroy();
producer.close();
}
}
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