Building production spark streaming applications

© Rocana, Inc. All Rights Reserved. | 1
Joey Echeverria, Platform Technical Lead - @fwiffo
Data Day Texas 2017
Building Production Spark Streaming
Applications

Joey
• Where I work: Rocana – Platform Technical Lead
• Where I used to work: Cloudera (’11-’15), NSA
• Distributed systems, security, data processing, big data

Context
• We built a system for large scale realtime collection, processing, and
analysis of event-oriented machine data
• On prem or in the cloud, but not SaaS
• Supportability is a big deal for us
• Predictability of performance under load and failures
• Ease of configuration and operation
• Behavior in wacky environments

Apache Spark Streaming

Spark streaming overview
• Stream processing API built on top of the Spark execution engine
• Micro-batching
• Every n-milliseconds fetch records from data source
• Execute Spark jobs on each input batch
• DStream API
• Wrapper around the RDD API
• Lets the developer think in terms of transformations on a stream of events

Input Batch
Spark Batch
Engine
Output Batch

Structured streaming
• New streaming API for Spark
• Re-use DataFrames API for streaming
• API was too new when we started
• First release was an alpha
• No Kafka support at the time
• Details won't apply, but the overall approach should be in the ballpark

Other notes
• Our experience is with Spark 1.6.2
• 2.0.0 was released after we started our Spark integration
• We use the Apache release of Spark
• Supports both CDH and HDP without recompiling
• We run Spark on YARN, so we're decoupled from other users on the cluster

Use Case
Real-time alerting on IT operational data

Our typical customer use cases
• >100K events / sec (8.6B events / day), sub-second end to end latency,
full fidelity retention, critical use cases
• Quality of service - “are credit card transactions happening fast enough?”
• Fraud detection - “detect, investigate, prosecute, and learn from fraud.”
• Forensic diagnostics - “what really caused the outage last friday?”
• Security - “who’s doing what, where, when, why, and how, and is that ok?”
• User behavior - ”capture and correlate user behavior with system
performance, then feed it to downstream systems in realtime.”

Overall architecture
weirdo formats
transformation 1
weirdo format -> event
avro events
transformation 2
event -> storage-specific
storage-specific representation of events

Real-time alerting
• Define aggregations, conditions, and actions
• Use cases:
• Send me an e-mail when the number of failed login events from a user is > 3
within an hour
• Create a ServiceNow ticket when CPU utilization spikes to > 95% for 10 minutes

UI

Architecture

Packaging, Deployment, and Execution

Packaging
• Application classes and dependencies
• Two options
• Shade all dependencies into an uber jar
• Make sure Hadoop and Spark dependencies are marked provided
• Submit application jars and dependent jars when submitting

Deployment modes
• Standalone
• Manually start up head and worker services
• Resource control depends on options selected when launching daemons
• Difficult to mix versions
• Apache Mesos
• Coarse grained run mode, launch executors as Mesos tasks
• Can use dynamic allocation to launch executors on demand
• Apache Hadoop YARN
• Best choice if your cluster is already running YARN

Spark on YARN
• Client mode versus cluster mode
• Client mode == Spark Driver on local server
• Cluster mode == Spark Driver in YARN AM
• Spark executors run in YARN containers (one JVM per executor)
• spark.executor.instances
• Each executor core uses one YARN vCore
• spark.executor.cores

Job submission
• Most documentation covers spark-submit
• OK for testing, but not great for production
• We use spark submitter APIs
• Built easier to use wrapper API
• Hide some of the details of configuration
• Some configuration parameters aren't respected when using submitter API
• spark.executor.cores, spark.executor.memory
• spark.driver.cores, spark.driver.memory

Job monitoring
• Streaming applications are always on
• Need to monitor the job for failures
• Restart the job on recoverable failures
• Notify an admin on fatal failures (e.g. misconfiguration)
• Validate as much up front as possible
• Our application runs rules through a type checker and query planner before
saving

Instrumentation, Metrics, and Monitoring

Instrumentation
You can't fix what
you don't measure

Instrumentation APIs
• Spark supports Dropwizard (née CodaHale) metrics
• Collect both application and framework metrics
• Supports most popular metric types
• Counters
• Gauges
• Histograms
• Timers
• etc.
• Use your own APIs
• Best option if you have your existing metric collection infrastructure

Custom metrics
• Implement the org.apache.spark.metrics.source.Source interface
• Register your source with sparkEnv.metricsSystem().registerSource()
• If you're measuring something during execution, you need to register the metric
on the executors
• Register executor metrics in a static block
• You can't register a metrics source until the SparkEnv has been initialized
SparkEnv sparkEnv = SparkEnv.get();
if (sparkEnv != null) {
// create and register source
}

Metrics collection
• Configure $SPARK_HOME/conf/metrics.properties
• Built-in sinks
• ConsoleSink
• CVSSink
• JmxSink
• MetricsServlet
• GraphiteSink
• Slf4jSink
• GangliaSink
• Or build your own

Build your own
• Implement the org.apache.spark.metrics.sink.Sink interface
• We built a KafkaEventSink that sends the metrics to a Kafka topic
formatted as Osso* events
• Our system has a metrics collector
• Aggregates metrics in a Parquet table
• Query and visualize metrics using SQL
• *http://www.osso-project.org

Report and visualize

Gotcha
• Due to the order of metrics subsystem initialization, your collection plugin
must be on the system classpath, not application classpath
• https://issues.apache.org/jira/browse/SPARK-18115
• Options:
• Deploy library on cluster nodes (e.g. add to HADOOP_CLASSPATH)
• Build a custom Spark assembly jar

Custom spark assembly
• Maven shade plugin
• Merge upstream Spark assembly JAR with your library and dependencies
• Shade/rename library packages
• Might break configuration parameters as well 
• *.sink.kafka.com_rocana_assembly_shaded_kafka_brokers
• Mark any dependencies already in the assembly as provided
• Ask me about our akka.version fiasco

Configuration and Tuning

Architecture

Predicting CPU/task resources
• Each output operation creates a separate batch job when processing a
micro-batch
• number of jobs = number of output ops
• Each data shuffle/re-partitioning creates a separate stage
• number of stages per job = number of shuffles + 1
• Each partition in a stage creates a separate task
• number of tasks per job = number of stages * number of partitions

Resources for alerting
• Each rule has a single output operation (write to Kafka)
• Each rule has 3 stages
1. Read from Kafka, project, filter and group data for aggregation
2. Aggregate values, filter (conditions) and group data for triggers
3. Aggregate trigger results and send trigger events to Kafka
• First stage partitions = number of Kafka partitions
• Stage 2 and 3 use spark.default.parallelism partitions

Example
• 100 rules, Kafka partitions = 50, spark.default.parallelism = 50
• number of jobs = 100
• number of stages per job = 3
• number of tasks per job = 3 * 50 = 150
• total number of tasks = 100 * 150 = 15,000

Task slots
• number of task slots = spark.executor.instances * spark.executor.cores
• Example
• 50 instances * 8 cores = 400 task slots

Waves
• The jobs processing the micro-batches will run in waves based on
available task slots
• Number of waves = total number of tasks / number of task slots
• Example
• Number of waves = 15,000 / 400 = 38 waves

Max time per wave
• maximum time per wave = micro-batch duration / number of waves
• Example:
• 15 second micro-batch duration
• maximum time per wave = 15,000 ms / 38 waves = 394 ms per wave
• If the average task time > 394 ms, then Spark streaming will fall behind

Monitoring batch processing time

Delay scheduling
• A technique of delaying scheduling of tasks to get better data locality
• Works great for long running batch tasks
• Not ideal for low-latency stream processing tasks
• Tip
• Set spark.locality.wait = 0ms
• Results
• Running job with 800 tasks on a very small (2 task slot) cluster, 300 event micro-batch
• With default setting: 402 seconds
• With 0ms setting: 26 seconds (15.5x faster)

Model memory requirements
• Persistent memory used by stateful operators
• reduceByWindow, reduceByKeyAndWindow
• countByWindow, countByValueAndWindow
• mapWithState, updateStateByKey
• Model retention time
• Built-in time-based retention (e.g. reduceByWindow)
• Explicit state management (e.g. org.apache.spark.streaming.State#remove())

Example
• Use reduceByKeyAndWindow to sum integers with a 30 second window
and 10 second slide over 10,000 keys
• active windows = window length / window slide
• 30s / 10s = 3
• estimated memory = active windows * num keys * (state size + key size)
• 3 *10,000 * (16 bytes + 80 bytes) = 2.75 MB

Monitor Memory

Putting it altogether
• Pick your packaging and deployment model based on operational needs,
not developer convenience
• Use Spark submitter APIs whenever possible
• Measure and report operational metrics
• Focus configuration and tuning on the expected behavior of your
application
• Model, configure, monitor

Questions?
@fwiffo | batman@rocana.com

Building production spark streaming applications

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