I had the pleasure of interning with the Apache Kudu team at Cloudera this summer. This project was my summer contribution to Kudu: a restructuring of the scan path to speed up queries.

This post discusses the Kudu Flume Sink. First, I’ll give some background on why we considered using Kudu, what Flume does for us, and how Flume fits with Kudu in our project.

Why Kudu

Traditionally in the Hadoop ecosystem we’ve dealt with various batch processing technologies such as MapReduce and the many libraries and tools built on top of it in various languages (Apache Pig, Apache Hive, Apache Oozie and many others). The main problem with this approach is that it needs to process the whole data set in batches, again and again, as soon as new data gets added. Things get really complicated when a few such tasks need to get chained together, or when the same data set needs to be processed in various ways by different jobs, while all compete for the shared cluster resources.

The opposite of this approach is stream processing: process the data as soon as it arrives, not in batches. Streaming systems such as Spark Streaming, Storm, Kafka Streams, and many others make this possible. But writing streaming services is not trivial. The streaming systems are becoming more and more capable and support more complex constructs, but they are not yet easy to use. All queries and processes need to be carefully planned and implemented.

To summarize, batch processing is:

• file-based
• a paradigm that processes large chunks of data as a group
• high latency and high throughput, both for ingest and query
• typically easy to program, but hard to orchestrate
• well suited for writing ad-hoc queries, although they are typically high latency

While stream processing is:

• a totally different paradigm, which involves single events and time windows instead of large groups of events
• still file-based and not a long-term database
• not batch-oriented, but incremental
• ultra-fast ingest and ultra-fast query (query results basically pre-calculated)
• not so easy to program, relatively easy to orchestrate
• impossible to write ad-hoc queries

And a Kudu-based near real-time approach is:

• flexible and expressive, thanks to SQL support via Apache Impala (incubating)
• a table-oriented, mutable data store that feels like a traditional relational database
• very easy to program, you can even pretend it’s good old MySQL
• low-latency and relatively high throughput, both for ingest and query

At Argyle Data, we’re dealing with complex fraud detection scenarios. We need to ingest massive amounts of data, run machine learning algorithms and generate reports. When we created our current architecture two years ago we decided to opt for a database as the backbone of our system. That database is Apache Accumulo. It’s a key-value based database which runs on top of Hadoop HDFS, quite similar to HBase but with some important improvements such as cell level security and ease of deployment and management. To enable querying of this data for quite complex reporting and analytics, we used Presto, a distributed query engine with a pluggable architecture open-sourced by Facebook. We wrote a connector for it to let it run queries against the Accumulo database. This architecture has served us well, but there were a few problems:

• we need to ingest even more massive volumes of data in real-time
• we need to perform complex machine-learning calculations on even larger data-sets
• we need to support ad-hoc queries, plus long-term data warehouse functionality

So, we’ve started gradually moving the core machine-learning pipeline to a streaming based solution. This way we can ingest and process larger data-sets faster in the real-time. But then how would we take care of ad-hoc queries and long-term persistence? This is where Kudu comes in. While the machine learning pipeline ingests and processes real-time data, we store a copy of the same ingested data in Kudu for long-term access and ad-hoc queries. Kudu is our data warehouse. By using Kudu and Impala, we can retire our in-house Presto connector and rely on Impala’s super-fast query engine.

But how would we make sure data is reliably ingested into the streaming pipeline and the Kudu-based data warehouse? This is where Apache Flume comes in.

Why Flume

According to their website “Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms.” As you can see, nowhere is Hadoop mentioned but Flume is typically used for ingesting data to Hadoop clusters.

Flume has an extensible architecture. An instance of Flume, called an agent, can have multiple channels, with each having multiple sources and sinks of various types. Sources queue data in channels, which in turn write out data to sinks. Such pipelines can be chained together to create even more complex ones. There may be more than one agent and agents can be configured to support failover and recovery.

Flume comes with a bunch of built-in types of channels, sources and sinks. Memory channel is the default (an in-memory queue with no persistence to disk), but other options such as Kafka- and File-based channels are also provided. As for the sources, Avro, JMS, Thrift, spooling directory source are some of the built-in ones. Flume also ships with many sinks, including sinks for writing data to HDFS, HBase, Hive, Kafka, as well as to other Flume agents.

In the rest of this post I’ll go over the Kudu Flume sink and show you how to configure Flume to write ingested data to a Kudu table. The sink has been part of the Kudu distribution since the 0.8 release and the source code can be found here.

Configuring the Kudu Flume Sink

Here is a sample flume configuration file:

agent1.sources  = source1
agent1.channels = channel1
agent1.sinks = sink1

agent1.sources.source1.type = exec
agent1.sources.source1.command = /usr/bin/vmstat 1
agent1.sources.source1.channels = channel1

agent1.channels.channel1.type = memory
agent1.channels.channel1.capacity = 10000
agent1.channels.channel1.transactionCapacity = 1000

agent1.sinks.sink1.type = org.apache.flume.sink.kudu.KuduSink
agent1.sinks.sink1.masterAddresses = localhost
agent1.sinks.sink1.tableName = stats
agent1.sinks.sink1.channel = channel1
agent1.sinks.sink1.batchSize = 50
agent1.sinks.sink1.producer = org.apache.kudu.flume.sink.SimpleKuduEventProducer

We define a source called source1 which simply executes a vmstat command to continuously generate virtual memory statistics for the machine and queue events into an in-memory channel1 channel, which in turn is used for writing these events to a Kudu table called stats. We are using org.apache.kudu.flume.sink.SimpleKuduEventProducer as the producer. SimpleKuduEventProducer is the built-in and default producer, but it’s implemented as a showcase for how to write Flume events into Kudu tables. For any serious functionality we’d have to write a custom producer. We need to make this producer and the KuduSink class available to Flume. We can do that by simply copying the kudu-flume-sink-<VERSION>.jar jar file from the Kudu distribution to the $FLUME_HOME/plugins.d/kudu-sink/lib directory in the Flume installation. The jar file contains KuduSink and all of its dependencies (including Kudu java client classes).

At a minimum, the Kudu Flume Sink needs to know where the Kudu masters are (agent1.sinks.sink1.masterAddresses = localhost) and which Kudu table should be used for writing Flume events to (agent1.sinks.sink1.tableName = stats). The Kudu Flume Sink doesn’t create this table, it has to be created before the Kudu Flume Sink is started.

You may also notice the batchSize parameter. Batch size is used for batching up to that many Flume events and flushing the entire batch in one shot. Tuning batchSize properly can have a huge impact on ingest performance of the Kudu cluster.

Here is a complete list of KuduSink parameters:

Let’s take a look at the source code for the built-in producer class:

public class SimpleKuduEventProducer implements KuduEventProducer {
  private byte[] payload;
  private KuduTable table;
  private String payloadColumn;

  public SimpleKuduEventProducer(){
  }

  @Override
  public void configure(Context context) {
    payloadColumn = context.getString("payloadColumn","payload");
  }

  @Override
  public void configure(ComponentConfiguration conf) {
  }

  @Override
  public void initialize(Event event, KuduTable table) {
    this.payload = event.getBody();
    this.table = table;
  }

  @Override
  public List<Operation> getOperations() throws FlumeException {
    try {
      Insert insert = table.newInsert();
      PartialRow row = insert.getRow();
      row.addBinary(payloadColumn, payload);

      return Collections.singletonList((Operation) insert);
    } catch (Exception e){
      throw new FlumeException("Failed to create Kudu Insert object!", e);
    }
  }

  @Override
  public void close() {
  }
}

SimpleKuduEventProducer implements the org.apache.kudu.flume.sink.KuduEventProducer interface, which itself looks like this:

public interface KuduEventProducer extends Configurable, ConfigurableComponent {
  /**
   * Initialize the event producer.
   * @param event to be written to Kudu
   * @param table the KuduTable object used for creating Kudu Operation objects
   */
  void initialize(Event event, KuduTable table);

  /**
   * Get the operations that should be written out to Kudu as a result of this
   * event. This list is written to Kudu using the Kudu client API.
   * @return List of {@link org.kududb.client.Operation} which
   * are written as such to Kudu
   */
  List<Operation> getOperations();

  /*
   * Clean up any state. This will be called when the sink is being stopped.
   */
  void close();
}

public void configure(Context context) is called when an instance of our producer is instantiated by the KuduSink. SimpleKuduEventProducer’s implementation looks for a producer parameter named payloadColumn and uses its value (“payload” if not overridden in Flume configuration file) as the column which will hold the value of the Flume event payload. If you recall from above, we had configured the KuduSink to listen for events generated from the vmstat command. Each output row from that command will be stored as a new row containing a payload column in the stats table. SimpleKuduEventProducer does not have any configuration parameters, but if it had any we would define them by prefixing it with producer. (agent1.sinks.sink1.producer.parameter1 for example).

The main producer logic resides in the public List<Operation> getOperations() method. In SimpleKuduEventProducer’s implementation we simply insert the binary body of the Flume event into the Kudu table. Here we call Kudu’s newInsert() to initiate an insert, but could have used Upsert if updating an existing row was also an option, in fact there’s another producer implementation available for doing just that: SimpleKeyedKuduEventProducer. Most probably you will need to write your own custom producer in the real world, but you can base your implementation on the built-in ones.

In the future, we plan to add more flexible event producer implementations so that creation of a custom event producer is not required to write data to Kudu. See here for a work-in-progress generic event producer for Avro-encoded Events.

Conclusion

Kudu is a scalable data store which lets us ingest insane amounts of data per second. Apache Flume helps us aggregate data from various sources, and the Kudu Flume Sink lets us easily store the aggregated Flume events into Kudu. Together they enable us to create a data warehouse out of disparate sources.

Ara Abrahamian is a software engineer at Argyle Data building fraud detection systems using sophisticated machine learning methods. Ara is the original author of the Flume Kudu Sink that is included in the Kudu distribution. You can follow him on Twitter at @ara_e.

Kudu 0.10 is shipping with a few important new features for range partitioning. These features are designed to make Kudu easier to scale for certain workloads, like time series. This post will introduce these features, and discuss how to use them to effectively design tables for scalability and performance.

The Apache Kudu team is happy to announce the release of Kudu 0.10.0!

This latest version adds several new features, including:

Welcome to the twentieth edition of the Kudu Weekly Update. This weekly blog post covers ongoing development and news in the Apache Kudu project.