Building a High-Performance Database with

Scala, Akka, and SparkEvan Chan

Who am I

User and contributor to Spark since 0.9, Cassandra since 0.6 Created Spark Job Server and FiloDB Talks at Spark Summit, Cassandra Summit, Strata, Scala Days, etc. http://velvia.github.io/

http://github.com/spark-jobserver/spark-jobserverhttp://github.com/filodb/FiloDBhttp://velvia.github.io/

Streaming is now King

Message Queue

EventsStream

Processing Layer

State / Database

Happy Users

Why are Updates Important?Appends

Streaming workloads. Add new data continuously.

Real data is *always* changing. Queries on live real-time data has business benefits.

Updates

Idempotency = really simple ingestion pipelines

Simpler streaming later

update late events (See Spark 2.0 Structured Streaming)

Introducing FiloDBA distributed, versioned, columnar analytics database. With updates. Built for streaming.

http://www.github.com/filodb/FiloDB

http://www.github.com/filodb/FiloDB

Fast Analytics Storage Scan speeds competitive with Apache Parquet

In-memory version significantly faster

Flexible filtering along two dimensions

Much more efficient and flexible partition key filtering

Efficient columnar storage using dictionary encoding and other techniques

Updatable

Spark SQL for easy BI integration

Message Queue

EventsSpark

Streaming

Short term storage, K-V

Adhoc, SQL, ML

Cassandra

FiloDB: Events, ad-hoc, batch

Spark

Dashboards, maps

100% Reactive Scala

Akka Cluster

Spark

Typesafe Config for all configuration

Scodec, Ficus, Enumeratum, Scalactic, etc.

Even most of the performance critical parts are written in Scala :)

Scala, Akka, and Spark Akka - eliminate shared mutable state

Remote and cluster makes building distributed client-server architectures easy

Backpressure, at-least-once is easy to build

Failure handling and supervision are critical for databases

Spark for SQL, DataFrames, ML, interfacing

One FiloDB Node

NodeCoordinatorActor (NCA)

DatasetCoordinatorActor (DsCA)

DatasetCoordinatorActor (DsCA)

Active MemTable

Flushing MemTableReprojector ColumnStore

Data, commands

Akka vs Futures

NodeCoordinatorActor (NCA)

DatasetCoordinatorActor (DsCA)

DatasetCoordinatorActor (DsCA)

Active MemTable

Flushing MemTableReprojector ColumnStore

Data, commands

Akka - control flow

Core I/O - Futures

Akka vs Futures Akka Actors:

External FiloDB node API (remote + cluster)

Async messaging with clients

State management and scheduling (flushing)

Futures:

Core I/O

Columnar data processing / ingestion

Type-safe processing stages

Akka for Control FlowDriver

Client

Executor

NCA

DsCA1 DsCA2

Executor

NCA

DsCA1 DsCA2

Flush()

NodeClusterActor

SingletonClusterProxy

Yes, Akka in Spark Columnar ingestion is stateful - need stickiness of state. This

is inherently difficult in Spark.

Akka (cluster) gives us a separate, asynchronous control channel to talk to FiloDB ingestors

Spark only gives data flow primitives, not async messaging

We need to route incoming records to the correct ingestion node. Sorting data is inefficient and forces all nodes to wait for sorting to be done.

On failure, can control state recovery and moving state

Data Ingestion SetupExecutor

NCA

DsCA1 DsCA2

task0 task1

Row Source Actor

Row Source Actor

Executor

NCA

DsCA1 DsCA2

task0 task1

Row Source Actor

Row Source Actor

Node Cluster Actor

Partition Map

FiloDB NodeFiloDB Node

FiloDB separate nodesExecutor

NCA

DsCA1 DsCA2

task0 task1

Row Source Actor

Row Source Actor

Executor

NCA

DsCA1 DsCA2

task0 task1

Row Source Actor

Row Source Actor

Node Cluster Actor

Partition Map

Akka wire protocol

Backpressure Assumes receiver is OK, starts sending rows

Allows configurable number of unacked messages before stops sending

Acking is receivers way of rate-limiting

Automatic retries for at-least-once

NACK for when receiver must stop (out of memory or MemTable full)

Testing Akka Cluster MultiNodeSpec / sbt-multi-jvm

AWESOME

Test multi-node message routing

Test cluster membership and subscription

Inject network failures

Core: All Futures /** * Clears all data from the column store for that given projection, for all versions. * More like a truncation, not a drop. * NOTE: please make sure there are no reprojections or writes going on before calling this */ def clearProjectionData(projection: Projection): Future[Response]

/** * Completely and permanently drops the dataset from the column store. * @param dataset the DatasetRef for the dataset to drop. */ def dropDataset(dataset: DatasetRef): Future[Response]

/** * Appends the ChunkSets and incremental indices in the segment to the column store. * @param segment the ChunkSetSegment to write / merge to the columnar store * @param version the version # to write the segment to * @return Success. Future.failure(exception) otherwise. */ def appendSegment(projection: RichProjection, segment: ChunkSetSegment, version: Int): Future[Response]

Kamon Tracing def appendSegment(projection: RichProjection, segment: ChunkSetSegment, version: Int): Future[Response] = Tracer.withNewContext("append-segment") { val ctx = Tracer.currentContext stats.segmentAppend() if (segment.chunkSets.isEmpty) { stats.segmentEmpty() return(Future.successful(NotApplied)) } for { writeChunksResp responses.head } } }

Kamon Tracing http://kamon.io

One trace can encapsulate multiple Future steps all executing on different threads

Tunable tracing levels

Summary stats and histograms for segments

Super useful for production debugging of reactive stack

http://kamon.io

Kamon Metrics

Uses HDRHistogram for much finer and more accurate buckets

Built-in metrics for Akka actors, Spray, Akka-Http, Play, etc. etc.

KAMON trace name=append-segment n=2863 min=765952 p50=2113536 p90=3211264 p95=3981312 p99=9895936 p999=16121856 max=19529728KAMON trace-segment name=write-chunks n=2864 min=436224 p50=1597440 p90=2637824 p95=3424256 p99=9109504 p999=15335424 max=18874368KAMON trace-segment name=write-index n=2863 min=278528 p50=432128 p90=544768 p95=598016 p99=888832 p999=2260992 max=8355840

Validation: Scalactic private def getColumnsFromNames(allColumns: Seq[Column], columnNames: Seq[String]): Seq[Column] Or BadSchema = { if (columnNames.isEmpty) { Good(allColumns) } else { val columnMap = allColumns.map { c => c.name -> c }.toMap val missing = columnNames.toSet -- columnMap.keySet if (missing.nonEmpty) { Bad(MissingColumnNames(missing.toSeq, "projection")) } else { Good(columnNames.map(columnMap)) } } }

for { computedColumns

Machine-Speed Scalahttp://github.com/velvia/filo

https://github.com/filodb/FiloDB/blob/new-storage-format/core/src/main/scala/filodb.core/binaryrecord/BinaryRecord.scala

http://github.com/velvia/filohttps://github.com/filodb/FiloDB/blob/new-storage-format/core/src/main/scala/filodb.core/binaryrecord/BinaryRecord.scala

Filo: High Performance Binary Vectors

Designed for NoSQL, not a file format

random or linear access

on or off heap

missing value support

Scala only, but cross-platform support possible

http://github.com/velvia/filo is a binary data vector library designed for extreme read performance with minimal deserialization costs.

http://github.com/velvia/filo

Billions of Ops / Sec

JMH benchmark: 0.5ns per FiloVector element access / add

2 Billion adds per second - single threaded

Who said Scala cannot be fast?

Spark API (row-based) limits performance significantly

val randomInts = (0 until numValues).map(i => util.Random.nextInt) val randomIntsAray = randomInts.toArray val filoBuffer = VectorBuilder(randomInts).toFiloBuffer val sc = FiloVector[Int](filoBuffer) @Benchmark @BenchmarkMode(Array(Mode.AverageTime)) @OutputTimeUnit(TimeUnit.MICROSECONDS) def sumAllIntsFiloApply(): Int = { var total = 0 for { i

Thank you Scala OSS!