Data processing platforms with SMACK: Spark and Mesos internals

Decomposing SMACK Stack
Spark & Mesos Internals
Anton Kirillov Apache Spark Meetup
intro by Sebastian Stoll Oooyala, March 2016

Who is this guy?
@antonkirillov
● Staff Engineer in Data Team @ Ooyala
● Scala programmer
● Focused on distributed systems
● Building data platforms with SMACK/Hadoop
● Ph.D. in Computer Science
● blog: datastrophic.io
● github: github.com/datastrophic

Roadmap
● Intro to Ooyala stack
● SMACK stack
○ Overview
○ Architecture design options
● Spark
○ Core concepts & execution workflow
○ Architecture
● Mesos
○ Cluster resource management
○ Architecture and scheduling
○ Frameworks
○ Spark on Mesos

SMACK Stack Overview
components and architecture designs

SMACK Stack
● Spark - a generalized framework for distributed data processing
supporting in-memory data caching and reuse across computations
● Mesos - cluster resource management system that provides efficient
resource isolation and sharing across distributed applications
● Akka - a toolkit and runtime for building highly concurrent, distributed,
and resilient message-driven applications on the JVM
● Cassandra - distributed, highly available database designed to handle
large amounts of data across multiple datacenters
● Kafka - a high-throughput, low-latency distributed messaging system
designed for handling real-time data feeds

Storage Layer: Cassandra
● Pros:
○ optimized for heavy write
loads
○ configurable CA (CAP)
○ linearly scalable
○ XDCR support
○ easy cluster resizing and
inter-DC data migration
● Cons:
○ data model (distributed
nested sorted map)
○ designed for fast serving
but not batch processing
○ not well-suited for ad-hoc
queries against historical
raw data

Fixing NoSQL limitations with Spark
//joining raw events with rolled-up and grouping by type
sqlContext.sql {"""
SELECT
events.campaignId,
events.eventType,
events.value + campaigns.total as total_events
FROM events
JOIN campaigns
ON events.campaignId = campaigns.id AND events.eventType = campaigns.eventType
""".stripMargin
}.registerTempTable("joined")
sqlContext.sql {"""
SELECT campaignId, eventType, sum(total_events) as total
FROM joined
GROUP BY campaignId, eventType
""".stripMargin
}.saveAsCassandraTable(”keyspace”, ”totals”)

Architecture of Spark/Cassandra Clusters
Separate Write & Analytics:
● clusters can be scaled
independently
● data is replicated by
Cassandra asynchronously
● Analytics has different
Read/Write load patterns
● Analytics contains additional
data and processing results
● Spark resource impact
limited to only one DC
To fully facilitate Spark-C* connector data locality awareness,
Spark workers should be collocated with Cassandra nodes (gotcha: CL=ONE)

Mesos as Spark cluster manager
● fine-grained resource
sharing between Spark
and other applications
● scalable partitioning
between multiple
instances of Spark
● unified platform for
running various
applications
(frameworks)
● fault-tolerant and
scalable

Stream Processing with Kafka and Spark
● be prepared for failures and broken data
● backup and patching strategies should be designed upfront
● patch/restore if time interval could be done by replay if store is idempotent

Spark Streaming with Kafka
val streamingContext = new StreamingContext(sc.getConf, Seconds(10))
val eventStream = KafkaUtils.createStream(
ssc = streamingContext,
zkQuorum = "zoo01,zoo02,zoo03",
groupId = "spark_consumer",
topics = Map("raw_events" -> 3)
)
eventStream.map(_.toEvent)
.saveToCassandra(keyspace, table)
streamingContext.start()
streamingContext.awaitTermination()

Data Ingestion with Akka
● actor model implementation
for JVM
● message-based and
asynchronous
● easily scalable from one
process to cluster of
machines
● actor hierarchies with
parental supervision
● easily packages in Docker to
be run on Mesos

Akka Http microservice
val config = new ProducerConfig(KafkaConfig())
lazy val producer = new KafkaProducer[A, A](config)
val routes: Route = {
post{
decodeRequest{
entity(as[String]){ str =>
JsonParser.parse(str).validate[Event] match {
case s: JsSuccess[String] =>
producer.send(new KeyedMessage(topic, str))
system.actorOf(Props[CassandraWriter]) ! s.get
case e: JsError => BadRequest -> JsError.toFlatJson(e).toString()
}
}
}
}
}
object AkkaHttpMicroservice extends App with Service {
Http().bindAndHandle(routes, config.getString("http.interface"), config.getInt("http.port"))
}

Writing to Cassandra with Akka
class CassandraWriterActor extends Actor with ActorLogging {
//for demo purposes, session initialized here
val session = Cluster.builder()
.addContactPoint("cassandra.host")
.build()
.connect()
override def receive: Receive = {
case event: Event =>
val statement = new SimpleStatement(event.createQuery)
.setConsistencyLevel(ConsistencyLevel.QUORUM)
Try(session.execute(statement)) match {
case Failure(ex) => //error handling code
case Success => sender ! WriteSuccessfull
}
}
}

Lambda Architecture with SMACK
● when design meets reality it’s hard to implement canonical architecture
● depending on the use case it’s easy to implement Kappa architecture as well

SMACK stack:
● concise toolbox for wide variety of data processing scenarios
● battle-tested and widely used software with large communities
● easy scalability and replication of data while preserving low latencies
● unified cluster management for heterogeneous loads
● single platform for any kind of applications
● implementation platform for different architecture designs
● really short time-to-market (e.g. for MVP verification)

Apache Spark in Depth
core concepts, architecture & internals

Meet Spark
● Generalized framework for distributed data processing (batch, graph, ML)
● Scala collections functional API for manipulating data at scale
● In-memory data caching and reuse across computations
● Applies set of coarse-grained transformations over partitioned data
● Failure recovery relies on lineage to recompute failed tasks
● Supports majority of input formats and integrates with Mesos / YARN

Spark makes data engineers happy
Backup/restore of Cassandra tables in Parquet
def backup(config: Config) {
sc.cassandraTable(config.keyspace, config.table).map(_.toEvent).toDF()
.write.parquet(config.path)
}
def restore(config: Config) {
sqlContext.read.parquet(config.path)
.map(_.toEvent).saveToCassandra(config.keyspace, config.table)
}
Query different data sources to identify discrepancies
sqlContext.sql {
"""
SELECT count()
FROM cassandra_event_rollups
JOIN mongo_event_rollups
ON cassandra_event_rollups.uuid = cassandra_event_rollups.uuid
WHERE cassandra_event_rollups.value != cassandra_event_rollups.value
""".stripMargin
}

RDD: Resilient Distributed Dataset
● A fault-tolerant, immutable, parallel data structure
● Provides API for
○ manipulating the collection of elements (transformations and materialization)
○ persisting intermediate results in memory for later reuse
○ controlling partitioning to optimize data placement
● Can be created through deterministic operation
○ from storage (distributed file system, database, plain file)
○ from another RDD
● Stores information about parent RDDs
○ for execution optimization and operations pipelining
○ to recompute the data in case of failure

RDD: a developer’s view
● Distributed immutable data + lazily evaluated operations
○ partitioned data + iterator
○ transformations & actions
● An interface defining 5 main properties
a list of partitions (e.g. splits in Hadoop)
def getPartitions: Array[Partition]
a list of dependencies on other RDDs
def getDependencies: Seq[Dependency[_]]
a function for computing each split
def compute(split: Partition, context: TaskContext): Iterator[T]
(optional) a list of preferred locations to compute each split on
def getPreferredLocations(split: Partition): Seq[String] = Nil
(optional) a partitioner for key-value RDDs
val partitioner: Option[Partitioner] = None
lineage
execution optimization

RDDs Example
● HadoopRDD
○ getPartitions = HDFS blocks
○ getDependencies = None
○ compute = load block in memory
○ getPrefferedLocations = HDFS block locations
○ partitioner = None
● MapPartitionsRDD
○ getPartitions = same as parent
○ getDependencies = parent RDD
○ compute = compute parent and apply map()
○ getPrefferedLocations = same as parent
○ partitioner = None
sparkContext.textFile("hdfs://...")

RDD Operations
● Transformations
○ apply user function to every element in a partition (or to the whole partition)
○ apply aggregation function to the whole dataset (groupBy, sortBy)
○ introduce dependencies between RDDs to form DAG
○ provide functionality for repartitioning (repartition, partitionBy)
● Actions
○ trigger job execution
○ used to materialize computation results
● Extra: persistence
○ explicitly store RDDs in memory, on disk or off-heap (cache, persist)
○ checkpointing for truncating RDD lineage

Execution workflow
29
rdd1.join(rdd2)
.groupBy(...)
.filter(...)
splits graph into
stages of tasks
submits each stage
as ready
launches tasks via
cluster manager
retries failed or
struggling tasks
executes tasks
stores and serves
blocks

Code sample: joining aggregated and raw data
//aggregate events after specific date for given campaign
val events = sc.cassandraTable("demo", "event")
.map(_.toEvent)
.filter(event => event.campaignId == campaignId && event.time.isAfter(watermark))
.keyBy(_.eventType)
.reduceByKey(_ + _)
.cache()
//aggregate campaigns by type
val campaigns = sc.cassandraTable("demo", "campaign")
.map(_.toCampaign)
.filter(campaign => campaign.id == campaignId && campaign.time.isBefore(watermark))
.keyBy(_.eventType)
.reduceByKey(_ + _)
.cache()
//joined rollups and raw events
val joinedTotals = campaigns.join(events)
.map { case (key, (campaign, event)) => CampaignTotals(campaign, event) }
.collect()
//count totals separately
val eventTotals = events.map{ case (t, e) => s"$t -> ${e.value}" }.collect()
val campaignTotals = campaigns.map{ case (t, e) => s"$t -> ${e.value}" }.collect()

Dependency types
● Narrow (pipelineable)
○ each partition of the parent RDD is used by at most
one partition of the child RDD
○ allow for pipelined execution on one cluster node
○ failure recovery is more efficient as only lost parent
partitions need to be recomputed
● Wide (shuffle)
○ multiple child partitions may depend on one parent
partition
○ require data from all parent partitions to be available
and to be shuffled across the nodes
○ if some partition is lost from all the ancestors a
complete recomputation is needed

Stages and Tasks
● Stages breakdown strategy
○ check backwards from final RDD
○ add each “narrow” dependency to
the current stage
○ create new stage when there’s a
shuffle dependency
● Tasks
○ ShuffleMapTask partitions its
input for shuffle
○ ResultTask sends its output to
the driver

Shuffle
● Shuffle Write
○ redistributes data among partitions
and writes files to disk
○ each shuffle task creates one file
with regions assigned to reducer
○ sort shuffle uses in-memory sorting
with spillover to disk to get final
result
● Shuffle Read
○ fetches the files and applies
reduce() logic
○ if data ordering is needed then it is
sorted on “reducer” side for any
type of shuffle

Sort Shuffle
● Incoming records accumulated
and sorted in memory according
their target partition ids
● Sorted records are written to file
or multiple files if spilled and
then merged
● index file stores offsets of the
data blocks in the data file
● Sorting without deserialization is
possible under certain conditions
(SPARK-7081)

Memory Management in Spark 1.6
● Execution Memory
○ storage for data needed during tasks execution
○ shuffle-related data
● Storage Memory
○ storage of cached RDDs and broadcast variables
○ possible to borrow from execution memory
(spill otherwise)
○ safeguard value is 0.5 of Spark Memory when cached
blocks are immune to eviction
● User Memory
○ user data structures and internal metadata in Spark
○ safeguarding against OOM
● Reserved memory
○ memory needed for running executor itself and not
strictly related to Spark

Execution Modes
● spark-shell --master [ local | spark | yarn-client | mesos]
○ launches REPL connected to specified cluster manager
○ always runs in client mode
● spark-submit --master [ local | spark:// | mesos:// | yarn ] spark-job.jar
○ launches assembly jar on the cluster
● Masters
○ local[k] - run Spark locally with K worker threads
○ spark - launches driver app on Spark Standalone installation
○ mesos - driver will spawn executors on Mesos cluster (deploy-mode: client | cluster)
○ yarn - same idea as with Mesos (deploy-mode: client | cluster)
● Deploy Modes
○ client - driver executed as a separate process on the machine where it has been launched and
spawns executors
○ cluster - driver launched as a container using underlying cluster manager

Apache Mesos
architecture, scheduling, frameworks & Spark

Cluster Resource Managers: Requirements
● Efficiency
○ efficient sharing of resources across applications
○ utilization of cluster resources in the most optimal manner
● Flexibility
○ support of wide array of current and future frameworks
○ dealing with hardware heterogeneity
○ support of resource requests of different types
● Scalability
○ scaling to clusters of dozens of thousands of nodes
○ scheduling system’s response times must remain acceptable while
increasing number of machines and applications
● Robustness
○ fault-tolerant guarantees for the system and applications
○ high availability of central scheduler component

Cluster Manager Architectures
source: Omega: flexible, scalable schedulers for large compute clusters

Mesos Architecture
● Master
○ a mediator between slave
resources and frameworks
○ enables fine-grained sharing of
resources by making resource
offers
● Slave
○ manages resources on physical
node and runs executors
● Framework
○ application that solves a specific
use case
○ Scheduler negotiates with master
and handles resource offers
○ Executors consume resources and
run tasks on slaves

Two-Level Scheduling
● Slave nodes report to Master
amount of available resources
● Allocation module starts offering
resources to frameworks
● Framework receives offers
○ if resources do not satisfy its
needs - rejects the offer
○ if resources satisfy its
demands - creates list of
tasks and sends to master
● Master verifies tasks and forwards
to executor (and launches the
executor if it’s not running)

Resource offer
id: { value: "0cb2328a-61c2-4316-91ef-cbbb6ebbf504-O1" }
framework_id: { value: "0cb2328a-61c2-4316-91ef-cbbb6ebbf504-0001" }
slave_id: { value: "0cb2328a-61c2-4316-91ef-cbbb6ebbf504-S0" }
hostname: "mesos-slave"
resources { name: "cpus", type: SCALAR, scalar { value: 6.0 }, role: "*" }
resources { name: "mem", type: SCALAR, scalar { value: 6762.0 }, role: "*" }
resources { name: "disk", type: SCALAR, scalar { value: 13483.0 }, role: "*" }
resources { name: "ports", type: RANGES, ranges { range { begin: 31000, end: 32000 } }, role: "*" }
url {
scheme: "http"
address {
hostname: "mesos-slave"
ip: "172.18.0.5"
port: 5151
}
path: "/slave(1)"
}

Framework Scheduler
class SomeMesosScheduler extends Scheduler {
override def resourceOffers(driver: SchedulerDriver, offers: List[Offer]): Unit = {
for(offer <- offers){
stateLock.synchronized {
if(isOfferValid(offer)){
val executorInfo = buildExecutorInfo(driver, "Executor A"))
//amount of tasks is calculated to fully use resources from the offer
val tasks = buildTasks(offer, executorInfo)
driver.launchTasks(List(offer.getId), tasks)
} else {
driver.declineOffer(offer.getId)
}
}
}
}
//rest of the methods implementations go here
}

Dominant Resource Fairness (DRF)
● Dominant resource
○ a resource of specific type (cpu, ram, etc.) which is most demanded by a framework among
other resources it needs
○ the resource is identified as a share of the total cluster resources of the same type
● Dominant share
○ a share of dominant resource allocated to a framework in the cluster
● Example:
○ Cluster total: 9 CPU & 18 GB RAM
○ Framework A tasks need < 3 CPU, 1 GB > (or < 33% CPU, 5% RAM >)
○ Framework B tasks need < 1 CPU, 4 GB > (or < 11% CPU, 22% RAM >)
● DRF algorithm computes frameworks’ dominant shares and tries to maximize
the smallest dominant share in the system

DRF Demo
● 3 frameworks with < 8% CPU, 7.5% RAM > demand each
● Framework A < 33% CPU, 15% RAM >, Framework B < 16% CPU, 30% RAM >)
● Framework A < 33% CPU, 15% RAM >, Framework B < 16% CPU, 36% RAM >)

DRF properties
● Sharing incentive
○ Each user should be better off sharing the cluster, than exclusively using her own partition of
the cluster. Consider a cluster with identical nodes and n users. Then a user should not be
able to allocate more tasks in a cluster partition consisting of 1/n of all resources.
● Strategy-proofness
○ Users should not be able to benefit by lying about their resource demands. This provides
incentive compatibility, as a user cannot improve her allocation by lying.
● Envy-freeness
○ A user should not prefer the allocation of another user. This property embodies the notion of
fairness.
● Pareto efficiency
○ It should not be possible to increase the allocation of a user without decreasing the allocation
of at least another user. This property is important as it leads to maximizing system utilization
subject to satisfying the other properties.
source: Dominant Resource Fairness: Fair Allocation of Multiple Resource Types

Resource Reservation
● Goals:
○ allocate all single slave resources to one type of framework
○ divide cluster between several framework types or organisations
○ framework groups prioritization and guaranteed allocation
● Static reservation
○ slave node is configured on start (cannot be reserved for another role or unreserved)
--resources="cpus:4;mem:2048;cpus(spark):8;mem(spark):4096"
● Dynamic reservation
○ resources are reserved/unreserved within a respond to resource offer
Offer::Operation::Reserve
○ MESOS-2018
● Extras:
○ persistent volumes
○ multiple disk resources

Resource Isolation
● Goals:
○ running tasks isolation and capping of runtime resources
○ programmatic control over task resources
○ use images to allow different environments
● Docker containerizer
○ executed tasks are docker containers (e.g. microservices packed in Docker)
● Mesos containerizer (default)
○ Mesos-native (no dependencies on other technologies)
○ provides fine-grained controls (cgroups/namespaces)
○ provides disk usage limits controls
● Composing
○ allows using multiple containerizers together
○ the first containerizer supporting task configuration will be used to launch it

Ubiquitous frameworks: Marathon
● distributed init.d
● long running tasks
execution
● HA mode with ZooKeeper
● Docker executor
● REST API
51

Marathon: launching Chronos in Docker
curl -XPOST 'http://marathon:8080/v2/apps' -H 'Content-Type: application/json' -d '{
"id": "chronos",
"container": {
"type": "DOCKER",
"docker": {
"network": "HOST",
"image": "datastrophic/chronos:mesos-0.27.1-chronos-2.5",
"parameters": [
{ "key": "env", "value": "CHRONOS_HTTP_PORT=4400" },
{ "key": "env", "value": "CHRONOS_MASTER=zk://zookeeper:2181/mesos" },
{ "key": "env", "value": "CHRONOS_ZK_HOSTS=zookeeper:2181"}
]
}
},
"ports": [ 4400 ],
"cpus": 1,
"mem": 512,
"instances": 1
}'

Ubiquitous frameworks: Chronos
● distributed cron
● HA mode with ZooKeeper
● supports graphs of jobs
● sensitive to network failures
53

More Mesos frameworks
● Spark
● Hadoop
● Cassandra
● Kafka
● Myriad: YARN on Mesos
● Storm
● Samza
54

Spark on Mesos
● Coarse-grained mode(default)
○ Spark Executor is launched one per Slave
and acquires all available cores in cluster
○ Tasks are scheduled by Spark relying on its
RPC mechanism (Akka)
● Fine-grained mode
○ Spark Executor is launched one per Slave
with minimal resources needed (1 core)
○ Spark tasks are executed as Mesos tasks
and use Mesos semantics

Spark on Mesos
● Coarse-grained mode
● Fine-grained mode
/opt/spark/bin/spark-submit
--class io.datastrophic.demo.SparkJob
--master mesos://zk://zookeeper:2181/mesos
--conf "spark.cores.max=10"
/opt/jobs/spark-jobs-assembly.jar
--conf "spark.mesos.coarse=false"

Spark on Mesos vs. YARN
● Mesos (coarse-grained)
● YARN
--conf "spark.cores.max=100"
--master yarn
--num-executors 25
--executor-cores 4

Running Spark via Marathon
curl -XPOST 'http://marathon:8080/v2/apps' -H 'Content-Type: application/json' -d '{
"cmd": "/opt/spark/bin/spark-submit
--deploy-mode client
/opt/jobs/spark-jobs-assembly.jar",
"id": "spark-pi",
"cpus": 1,
"mem": 1024,
"instances": 1
}'

Running Spark via Chronos
curl -L -H 'Content-Type: application/json' -X POST http://mesos:4400/scheduler/iso8601 -d '{
"name": "Scheduled Spark Submit Job",
"/opt/spark/bin/spark-submit
/opt/jobs/spark-jobs-assembly.jar",
"shell": true,
"async": false,
"cpus": 0.1,
"disk": 256,
"mem": 1024,
"owner": "anton@datastrophic.io",
"description": "Spark Job executed every 3 minutes",
"schedule": "R/2016-03-14T12:35:00.000Z/PT3M"
}'

Spark deployment strategies
● Binaries distribution
○ every node in the cluster must have Spark libraries installed in the same locations
○ pros: easy to start with
○ cons: hard to upgrade, hard to have several Spark versions simultaneously
● Edge nodes
○ use nodes with specific environment setup which are reachable from Mesos cluster and keep
Spark executor jars in accessible location like S3, HTTP or HDFS
○ pros: easy to use multiple Spark versions, minimal dependencies on Mesos
○ cons: hard to maintain in case of multi-tenancy
● Dockerized environment
○ Instead of edge nodes use Docker containers with environment configured for specific needs
(hosts still need to be reachable from Mesos cluster) and use Docker Spark executor
○ pros: highly isolated environments for specific needs, could be upgraded independently, zero
impact on cluster nodes
○ cons: could be hard to properly setup and configure

Mesos Framework Walkthrough
● Throttler
○ a demo framework for load testing Cassandra
○ load intensity is controlled by parameters: total queries, queries per task and
parallelism (how many Mesos tasks to run in parallel)
● Goals
○ take a look at working (simple) Mesos application
○ see how Scheduler, Executor and framework launcher could be implemented
● Sources:
○ source code and dockerized Mesos cluster configuration are available at
github/datastrophic/mesos-workshop
○ all the examples (and even more) available as well

Questions
@antonkirillov datastrophic.io

Data processing platforms with SMACK: Spark and Mesos internals

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