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Kafka Summit - Introduction to Kafka Streams with a Real-Life Example

Kafka Summit - Introduction to Kafka Streams with a Real-Life Example

On our project, we built a great system to analyze customer records in real time. We pioneered a microservices architecture using Spark and Kafka and we had to tackle many technical challenges. In this session, I will show how Kafka Streams provided a great replacement to Spark Streaming and I will explain how to use this great library to implement low latency data pipelines.

Alexis Seigneurin

May 08, 2017

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    WITH A REAL-LIFE EXAMPLE Alexis Seigneurin
  2. Who I am • Software engineer for 15+ years •

    Consultant at Ippon USA, previously at Ippon France • Favorite subjects: Spark, Kafka, Machine Learning, Scala • Spark certified • @aseigneurin
  3. • 200+ software engineers in France, the US and Australia

    • In the US: NYC, DC, RVA • Digital, Big Data and Cloud applications • Java & Agile expertise • Open-source projects: JHipster, Tatami, etc. • @ipponusa
  4. The project • Analyze records from customers → Send notifications

    • High volume of data • 25 millions records per day in average + with seasonal peaks • Need to keep at least 60 days of history = 1.5 Billion records • Need an hybrid platform • Batch processing for some types of analysis • Streaming for other analyses • Hybrid team • Data Scientists: more familiar with Python • Software Engineers: Java
  5. Architecture - Real time platform • New detectors are implemented

    by Data Scientists all the time • Need the implementations to be independent from each other • One Spark Streaming job per detector • Microservice-inspired architecture • Diamond-shaped • Upstream jobs are written in Scala • Core is made of multiple Python jobs, one per detector • Downstream jobs are written in Scala • Plumbing between the jobs → Kafka 1/2
  6. Modularity • One Spark job per detector • Hot deployments:

    can roll out new detectors (= new jobs) without stopping existing jobs • Can roll out updated code without affecting other jobs • Able to measure the resources consumed by a single job • Shared services are provided by upstream and downstream jobs
  7. Spark 1.x + Kafka? • Spark has become the de-facto

    processing framework • Provides APIs for multiple programming languages • Python → Data Scientists • Scala/Java → Software Engineers • Supports batch jobs and streaming jobs, incl. support for Kafka…
  8. Consuming from Kafka • Connecting Spark to Kafka, 2 methods:

    • Receiver-based approach: not ideal for parallelism • Direct approach: better for parallelism but have to deal with Kafka offsets Spark + Kakfa problem s
  9. Dealing with Kafka offsets • Default: consumes from the end

    of the Kafka topic (or the beginning) • Documentation → Use checkpoints • Tasks have to be Serializable (not always possible: dependent libraries) • Harder to deploy the application (classes are serialized) → run a new instance in parallel and kill the first one (harder to automate; messages consumed twice) • Requires a shared file system (HDFS, S3) → big latency on these FS that forces to increase the micro-batch interval 1/2 Spark + Kakfa problem s
  10. Dealing with Kafka offsets • Dealing with Kafka offsets •

    Solution: deal with offsets in the Spark Streaming application • Write the offsets to a reliable storage: ZooKeeper, Kafka… • Write after processing the data • Read the offsets on startup (if no offsets, start from the end) • blog.ippon.tech/spark-kafka-achieving-zero-data-loss/ 2/2 Spark + Kakfa problem s
  11. Micro-batches Spark streaming processes events in micro-batches • Impact on

    latency • Spark Streaming micro-batches → hard to achieve sub-second latency • See spark.apache.org/docs/latest/streaming-programming-guide.html#task-launching-overheads • Total latency of the system = sum of the latencies of each stage • In this use case, events are independent from each other - no need for windowing computation → a real streaming framework would be more appropriate • Impact on memory usage • Kafka+Spark using the direct approach = 1 RDD partition per Kafka partition • If you start the Spark with lots of unprocessed data in Kafka, RDD partitions can exceed the size of the memory Spark + Kakfa problem s
  12. Allocation of resources in Spark • With Spark Streaming, resources

    (CPU & memory) are allocated per job • Resources are allocated when the job is submitted and cannot be updated on the fly • Have to allocate 1 core to the Driver of the job → unused resource • Have to allocate extra resources to each job to handle variations in traffic → unused resources • For peak periods, easy to add new Spark Workers but jobs have to restarted • Idea to be tested: • Over allocation of real resources, e.g let Spark know it has 6 cores on a 4-cores server Spark + Kakfa problem s
  13. Python code in production • Data Scientists know Python →

    They can contribute • But shipping code written by Data Scientists is not ideal • Need production-grade code (error handling, logging…) • Code is less tested than Scala code • Harder to deploy than a JAR file → Python Virtual Environments • blog.cloudera.com/blog/2015/09/how-to-prepare-your-apache- hadoop-cluster-for-pyspark-jobs/ Spark + Kakfa problem s
  14. Resilience of Spark Jobs • Spark Streaming application = 1

    Driver + 1 Application • Application = N Executors • If an Executor dies → restarted (seamless) • If the Driver dies, the whole Application must be restarted • Scala/Java jobs → “supervised” mode • Python jobs → not supported with Spark Standalone Spark + Kakfa problem s
  15. Writing to Kafka • Spark Streaming comes with a library

    to read from Kafka but none to write to Kafka! • Flink or Kafka Streams do that out-of-the-box • Cloudera provides an open-source library: • github.com/cloudera/spark-kafka-writer • (Has been removed by now!) Spark + Kakfa problem s
  16. Spark 2.x + Kafka? • New API: Structured Streaming •

    Still ALPHA in 2.1 • Support is improving…
  17. Kafka Streams docs.confluent.io/3.2.0/streams/index.html • “powerful, easy-to-use library for building highly

    scalable, fault-tolerant, distributed stream processing applications on top of Apache Kafka” • Works with Kafka ≥ 0.10 • No cluster needed: Kafka is the cluster manager (consumer groups) • Natively consumes messages from Kafka (and handles offsets) • Natively pushes produced messages to Kafka • Processes messages one at a time → low latency, low footprint • Java library (works best in Java, can work in Scala)
  18. • Read text from a topic • Process the text:

    • Only keep messages containing the “a” character • Capitalize the text • Output the result to another topic Quick Example 1/3
  19. • Create a regular Java application (with a main) •

    Add the Kafka Streams dependency: <dependency>
 </dependency> • Add the Kafka Streams code (next slide) • Build and run the JAR Quick Example 2/3
  20. Properties props = new Properties();
 props.put(StreamsConfig.APPLICATION_ID_CONFIG, “text-transformer");
 props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");

 props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, "8");
 props.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "earliest"); 
 KStreamBuilder builder = new KStreamBuilder(); 
 builder.stream(Serdes.String(), Serdes.String(), "text-input")
 .filter((key, value) -> value.contains("a"))
 .mapValues(text -> text.toUpperCase())
 .to(Serdes.String(), Serdes.String(), "text-output");
 KafkaStreams streams = new KafkaStreams(builder, props);
 streams.start(); Quick Example 3/3 • Application ID = Kafka consumer group • Threads for parallel processing (relates to partitions) • Topic to read from + key/ value deserializers • Transformations: map, filter… • Topic to write to + key/value serializers
  21. Processor Topology • Need to define one or more processor

    topologies • Two APIs to define topologies: • DSL (preferred): map(), filter(), to()… • Processor API (low level): implement the Processor interface then connect source processors, stream processors and sink processors together
  22. Parallelism (one process) • Kafka Streams creates 1 task per

    partition in the input topic • A task is an instance of the topology • Tasks are independent from each other • The number of processing threads is determined by the developer props.put(StreamsConfig.NUM_STREAM_THREADS_CONFIG, "8"); • Tasks are distributed between threads 1/2
  23. Parallelism (one process) • 3 partitions → 3 tasks •

    The tasks are distributed to the 2 threads 2/2
  24. Parallelism (multiple processes) • With multiple processes (multiple instances of

    the JVM), each consumer process is assigned a portion of the partitions → Consumer group • Reassignment of partitions occurs: • When a new consumer joins the group • When a consumer dies → Tasks are created/deleted accordingly 1/2
  25. Parallelism (multiple processes) • Partitions are assigned to 2 consumers

    • 3 partitions → 3 tasks (as before) • Each thread has one task → Improved parallelism 2/2
  26. KStream vs, KTable KStream is a stream of records •

    Records are independent from each other • (Do not use log compaction) Example: 
 KStreamBuilder builder = new KStreamBuilder();
 KStream<String, String> stream = builder.stream(Serdes.String(), Serdes.String(), "input-topic"); Example (inspired from the documentation): • Sum values as records arrive • Records: • (alice, 1) = 1 • (charlie, 1) = 2 • (alice, 3) = 5 • → Adds to (alice, 1)
  27. KStream vs, KTable KTable is a change log stream •

    New records with the same key are an update of previously received records for the same key • Keys are required • Requires a state store Example: KStreamBuilder builder = new KStreamBuilder();
 KTable<String, String> table = builder.table(Serdes.String(), Serdes.String(), "input-topic", "store-name"); Example (inspired from the documentation): • Sum values as records arrive • Records: • (alice, 1) = 1 • (charlie, 1) = 2 • (alice, 3) = 4 • → Replaces (alice, 1)
  28. map / mapValues Apply a transformation to the records flatMap

    / flatMapValues Apply a transformation to the records and create 0/1/n records per input record filter Apply a predicate groupBy / groupByKey Group the records. Followed by a call to reduce, aggregate or count join / leftJoin / outerJoin Joins 2 KStreams / KTables to Writes the records to a Kafka topic through Writes the records to a Kafka topic and builds a new KStream / KTable from this topic API 1/2
  29. State Stores • Some operations require to store a state

    • KTables (by definition, they need to keep previously received values) • Aggregations (groupBy / groupByKey) • Windowing operations • One state store per task (RocksDB or a hash map) • Backed by internal topics for recovery → fault tolerance • Can be queried internally
  30. Aggregations • Aggregations are performed by keys • Records with

    a null key are ignored • Repartition the data using an internal topic if need be • Aggregations can be windowed or non-windowed • Aggregating a KStream generates a KTable builder.stream(dummySerde, accountSerde, "accounts")
 .map((_, account) => (account.accountId, account))
 .to(stringSerde, accountSerde, "accounts-by-id")
 builder.stream(stringSerde, accountSerde, "accounts-by-id")
  31. Joins • Types of joins: inner / outer / left

    join • Operands: • KTable to KTable → Non-windowed • KStream to KTable → Non-windowed • KStream to KStream → Windowed (to avoid an infinitely growing result) • Data must be co-partitioned • Repartition using an internal topic if need be
  32. Deploying and Running • Assemble a JAR (maven-shade plugin) •

    Run the JAR as a regular Java application (java -cp …) • Make sure all instances are in the same consumer group (same application ID)
  33. Running Topic “AUTH-JSON” with 4 partitions Application ID = “auth-converter”

    Log on the first instance: 11:00:22,331 ...AbstractCoordinator - Successfully joined group auth-converter with generation 1 11:00:22,332 ...ConsumerCoordinator - Setting newly assigned partitions [AUTH_JSON-2, AUTH_JSON-1, AUTH_JSON-3, AUTH_JSON-0] for group auth-converter
  34. Running - Scaling up Start a new instance: Log on

    the first instance: 11:01:31,402 ...AbstractCoordinator - Successfully joined group auth-converter with generation 2 11:01:31,404 ...ConsumerCoordinator - Setting newly assigned partitions [AUTH_JSON-2, AUTH_JSON-3] for group auth-converter 11:01:31,390 ...ConsumerCoordinator - Revoking previously assigned partitions [AUTH_JSON-2, AUTH_JSON-1, AUTH_JSON-3, AUTH_JSON-0] for group auth-converter 11:01:31,401 ...ConsumerCoordinator - Setting newly assigned partitions [AUTH_JSON-1, AUTH_JSON-0] for group auth-converter
  35. Running - Scaling down Kill one of the instances Log

    on the remaining instance: 11:02:13,410 ...ConsumerCoordinator - Revoking previously assigned partitions [AUTH_JSON-1, AUTH_JSON-0] for group auth-converter 11:02:13,415 ...ConsumerCoordinator - Setting newly assigned partitions [AUTH_JSON-2, AUTH_JSON-1, AUTH_JSON-3, AUTH_JSON-0] for group auth-converter
  36. Delivery semantics • At least once • No messages will

    be lost • Messages can be processed a second time when failure happens → Make your system idempotent • Exactly once planned for Kafka 0.11 • KIP-98 - Exactly Once Delivery and Transactional Messaging • KIP-129: Streams Exactly-Once Semantics
  37. Migration • Conversion of Spark / Scala code • Upgraded

    from Scala 2.10 to 2.11 and enabled the -Xexperimental flag of the Scala compiler so that Scala lambdas are converted into Java lambdas (SAM support) • Removed lots of specific code to read from / write to Kafka (supported out-of-the-box with Kafka Streams) • API similar to the RDD API → Very straightforward conversion (no need to call foreachRDD, so even better!) • Conversion of Spark / Python code: not attempted
  38. Metrics • Kafka Streams doesn’t have a UI to display

    metrics (e.g. number of records processed) • Used Dropwizard Metrics (metrics.dropwizard.io) • Java API to calculate metrics and send them to various sinks • Used InfluxDB to store the metrics • Graphite compatible • Used Grafana to display the metrics as graphs
  39. Metrics aggregation • Each instance reports its own metrics →

    Need to aggregate metrics • Specific reporter to send Dropwizard Metrics to a Kafka topic • Kafka topic to collect metrics • 1 partition • Key = instance ID (e.g. app-1, app-2…) • Value = monotonic metric • Kafka Streams app to aggregate metrics • Input is a KTable (new values replace previous values) • Send aggregated metrics to InfluxDB
  40. Kafka Streams app to aggregate metrics KTable<String, CounterMetric> metricsStream =

    builder.table(appIdSerde, metricSerde, "metrics", "raw-metrics");
 KStream<String, CounterMetric> metricValueStream = metricsStream
 .groupBy((key, value) -> new KeyValue<>(value.getName(), value), metricNameSerde, metricSerde)
 .reduce(CounterMetric::add, CounterMetric::subtract, "aggregates")
 .toStream() .to(metricNameSerde, metricSerde, "metrics-agg");
 // --- Second topology
 GraphiteReporter graphite = GraphiteReporter.builder()
 KStream<String, CounterMetric> aggMetricsStream = builder.stream(metricNameSerde, metricSerde, "metrics-agg");
 aggMetricsStream.foreach((key, metric) -> graphite.send(metric));
  41. Send data into Kafka (1M records) Start consumer 1 Start

    consumer 2 Aggregated metric (from consumers 1 and 2) Stop consumer 2 Delta = records processed twice
  42. Results Pros • Simpler code (no manual handling of offsets)

    • Simpler packaging (no dependencies to exclude, less dependency version conflicts) • Much lower latency: from seconds to milliseconds • Reduced memory footprint • Easier scaling • Improved stability when restarting the application Cons • No UI • No centralized logs → Use ELK or equivalent… • No centralized metrics → Aggregate metrics • Have to use an intermediate topic if grouping the data by a value that is not the key
  43. Summary • Very easy to build pipelines on top of

    Kafka • Great fit for micro-services • Compared to Spark Streaming: • Better for realtime apps than Spark Streaming • Lower latency, lower memory footprint, easier scaling • Lower level: good for prod, lacks a UI for dev • Compared to a standard Kafka consumer: • Higher level: faster to build a sophisticated app • Less control for very fine-grained consumption