time series processing with solr and spark: presented by josef adersberger, qaware
TRANSCRIPT
O C T O B E R 1 1 - 1 4 , 2 0 1 6 • B O S T O N , M A
Time Series Processing with Solr and Spark Josef Adersberger (@adersberger)
CTO, QAware
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TIME SERIES 101
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01WE’RE SURROUNDED BY TIME SERIES
▸ Operational data: Monitoring data, performance metrics, log events, …
▸ Data Warehouse: Dimension time
▸ Measured Me: Activity tracking, ECG, …
▸ Sensor telemetry: Sensor data, …
▸ Financial data: Stock charts, …
▸ Climate data: Temperature, …
▸ Web tracking: Clickstreams, …
▸ …
@adersberger
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WE’RE SURROUNDED BY TIME SERIES (Pt. 2)
▸ Oktoberfest: Visitor and beer consumption trend
the singularity
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01TIME SERIES: BASIC TERMS
univariate time series multivariate time series multi-dimensional time series (time series tensor)
time series setobservation
@adersberger
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01ILLUSTRATIVE OPERATIONS ON TIME SERIES
align
Time series => Time series
diff downsampling outlier
min/max avg/med slope std-dev
Time series => Scalar
@adersberger
OUR USE CASE
Monitoring Data Analysis of a business-critical,worldwide distributed software system. Enableroot cause analysis and anomaly detection.
> 1,000 nodes worldwide
> 10 processes per node
> 20 metrics per process (OS, JVM, App-spec.)
Measured every second.
= about 6.3 trillions observations p.a.Data retention: 5 yrs.
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01USE CASE: EXPLORING
Drill-down host process measurements counters (metrics)
Query time series metadata
Superimpose time series
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01USE CASE: STATISTICS
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01USE CASE: ANOMALY DETECTION
Featuring Twitter Anomaly Detection (https://github.com/twitter/AnomalyDetectionand Yahoo EGDAS https://github.com/yahoo/egads
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01USE CASE: SQL AND ZEPPELIN
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01AVAILABLE TIME SERIES DATABASES
https://github.com/qaware/big-data-landscape
EASY-TO-USE BIG TIME SERIES DATA STORAGE & PROCESSING ON SPARK
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01THE CHRONIX STACK chronix.io
Big time series database Scale-out Storage-efficient Interactive queries
No separate servers: Drop-in to existing Solr and Spark installations
Integrated into the relevant open source ecosystem
@adersberger
Core
Chronix Storage
Chronix Server
Chronix Spark
Chr
onix
For
mat
GrafanaChronix Analytics
Collection
Analytics Frontends
Logstash fluentd collectd
Zeppelin
Prometheus Ingestion Bridge
KairosDB OpenTSDBInfluxDB Graphite
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node
Distributed Data &Data Retrieval ‣ Data sharding ‣ Fast index-based queries ‣ Efficient storage format
Distributed Processing ‣ Heavy lifting distributed
processing ‣ Efficient integration of Spark
and Solr
Result Processing Post-processing on a smaller set of time series
data flow
icon credits to Nimal Raj (database), Arthur Shlain (console) and alvarobueno (takslist)
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TIME SERIES MODEL
Set of univariate multi-dimensional numeric time series
▸ set … because it’s more flexible and better to parallelise if operations can input and output multiple time series.
▸ univariate … because multivariate will introduce too much complexity (and we have our set to bundle multiple time series).
▸ multi-dimensional … because the ability to slice & dice in the set of time series is very convenient for a lot of use cases.
▸ numeric … because it’s the most common use case.
A single time series is identified by a combination of its non-temporal dimensional values (e.g. unit “mem usage” + host “aws42” + process “tomcat”)
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01CHRONIX SPARK API: ENTRY POINTS
CHRONIX SPARK
ChronixRDD
ChronixSparkContext
‣ Represents a set of time series ‣Distributed operations on sets of time series
‣Creates ChronixRDDs ‣ Speaks with the Chronix Server (Solr)
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01CHRONIX SPARK API: DATA MODEL
MetricTimeSeries
MetricObservationDataFrame
+ toDataFrame()
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Dataset<MetricTimeSeries>
Dataset<MetricObservation>
+ toDataset() + toObservationsDataset()
ChronixRDD
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01SPARK APIs FOR DATA PROCESSING
RDD DataFrame Dataset
typed yes no yes
optimized medium highly highly
mature yes yes medium
SQL no yes no
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01CHRONIX RDD
Statistical operations
the set characteristic: a JavaRDD of MetricTimeSeries
Filter the set (esp. bydimensions)
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01METRICTIMESERIES DATA TYPEaccess all timestamps
the multi-dimensionality:get/set dimensions(attributes)
access all observations as stream
access all numeric values
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01//Create Chronix Spark context from a SparkContext / JavaSparkContextChronixSparkContext csc = new ChronixSparkContext(sc);//Read data into ChronixRDDSolrQuery query = new SolrQuery( "metric:\"java.lang:type=Memory/HeapMemoryUsage/used\""); ChronixRDD rdd = csc.query(query, "localhost:9983", //ZooKeeper host "chronix", //Solr collection for Chronix new ChronixSolrCloudStorage());//Calculate the overall min/max/mean of all time series in the RDDdouble min = rdd.min();double max = rdd.max();double mean = rdd.mean();
DataFrame df = rdd.toDataFrame(sqlContext);DataFrame res = df .select("time", "value", "process", "metric") .where("process='jenkins-jolokia'") .orderBy("time"); res.show();
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CHRONIX SPARK INTERNALS
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Distributed Data &Data Retrieval ‣ Data sharding (OK) ‣ Fast index-based queries (OK) ‣ Efficient storage format
@adersberger
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01CHRONIX FORMAT: CHUNKING TIME SERIES
TIME SERIES ‣ start: TimeStamp ‣ end: TimeStamp ‣ dimensions: Map<String, String> ‣ observations: byte[]
TIME SERIES ‣ start: TimeStamp ‣ end: TimeStamp ‣ dimensions: Map<String, String> ‣ observations: byte[]
Logical
TIME SERIES ‣ start: TimeStamp ‣ end: TimeStamp ‣ dimensions: Map<String, String> ‣ observations: byte[]
Physical
Chunking: 1 logical time series = n physical time series (chunks) 1 chunk = fixed amount of observations 1 chunk = 1 Solr document
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01CHRONIX FORMAT: ENCODING OF OBSERVATIONS
Binary encoding of all timestamp/value pairs (observations) with ProtoBuf incl. binary compression. Delta encoding leading to more effective binary compression
… of time stamps (DCC, Date-Delta-Compaction)
… of values: diff
chunck • timespan • nbr. of observations
periodic distributed time stamps (pts): timespan / nbr. of observations
real time stamps (rts) if |pts(x) - rts(x)| < threshold : rts(x) = pts(x) value_to_store = pts(x) - rts(x)
value_to_store = value(x) - value(x-1)
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01CHRONIX FORMAT: TUNING CHUNK SIZE AND CODEC
GZIP + 128
kBytes
Florian Lautenschlager, Michael Philippsen, Andreas Kumlehn, Josef AdersbergerChronix: Efficient Storage and Query of Operational Time Series International Conference on Software Maintenance and Evolution 2016 (submitted)
@adersberger
storage demand access
time
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01CHRONIX FORMAT: STORAGE EFFICIENCY BENCHMARK
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01CHRONIX FORMAT: PERFORMANCE BENCHMARK
unit: secondsnbr of queries query
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Distributed Processing ‣ Heavy lifting distributed
processing ‣ Efficient integration of Spark
and Solr
@adersberger
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01SPARK AND SOLR BEST PRACTICES: ALIGN PARALLELISM
SolrDocument(Chunk)
Solr Shard Solr Shard
TimeSeries TimeSeries TimeSeries TimeSeries TimeSeries
Partition Partition
ChronixRDD
• Unit of parallelism in Spark: Partition • Unit of parallelism in Solr: Shard • 1 Spark Partition = 1 Solr Shard
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
SolrDocument(Chunk)
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01ALIGN THE PARALLELISM WITHIN CHRONIXRDD
public ChronixRDD queryChronixChunks( final SolrQuery query, final String zkHost, final String collection, final ChronixSolrCloudStorage<MetricTimeSeries> chronixStorage) throws SolrServerException, IOException { // first get a list of replicas to query for this collection List<String> shards = chronixStorage.getShardList(zkHost, collection); // parallelize the requests to the shards JavaRDD<MetricTimeSeries> docs = jsc.parallelize(shards, shards.size()).flatMap( (FlatMapFunction<String, MetricTimeSeries>) shardUrl -> chronixStorage.streamFromSingleNode( new KassiopeiaSimpleConverter(), shardUrl, query)::iterator); return new ChronixRDD(docs);}
Figure out all Solr shards (using CloudSolrClient in the background)
Query each shard in parallel and convert SolrDocuments to MetricTimeSeries
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01SPARK AND SOLR BEST PRACTICES: PUSHDOWNSolrQuery query = new SolrQuery( “<Solr query containing filters and aggregations>"); ChronixRDD rdd = csc.query(query, …
@adersberger
Predicate pushdown • Pre-filter time series based on their metadata (dimensions, start, end) with Solr.
Aggregation pushdown • Perform pre-aggregations (min/max/avg/…) at ingestion time and store it as metadata.
• (to come) Perform aggregations on Solr-level at query time by enabling Solr to decode observations
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01SPARK AND SOLR BEST PRACTICES: EFFICIENT DATA TRANSFER
Reduce volume: Pushdown & compression
Use efficient protocols: Low-overhead, bulk, stream
Avoid remote transfer: Place Spark tasks (processes 1 partition) on the Solr node with the appropriate shard. (to come by using SolrRDD)
@adersberger
Export Handler
ChronixRDD
CloudSolrStream
Format Decoder
bulk of JSON tuples
Chronix SparkSolr / SolrJ
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private Stream<MetricTimeSeries> streamWithCloudSolrStream(String zkHost, String collection, String shardUrl, SolrQuery query, TimeSeriesConverter<MetricTimeSeries> converter) throws IOException { Map params = new HashMap(); params.put("q", query.getQuery()); params.put("sort", "id asc"); params.put("shards", extractShardIdFromShardUrl(shardUrl)); params.put("fl", Schema.DATA + ", " + Schema.ID + ", " + Schema.START + ", " + Schema.END + ", metric, host, measurement, process, ag, group"); params.put("qt", "/export"); params.put("distrib", false); CloudSolrStream solrStream = new CloudSolrStream(zkHost, collection, params); solrStream.open(); SolrTupleStreamingService tupStream = new SolrTupleStreamingService(solrStream, converter); return StreamSupport.stream( Spliterators.spliteratorUnknownSize(tupStream, Spliterator.SIZED), false);}
Pin query to one shard
Use export request handler
Boilerplate code to stream response
@adersberger
Time Series Databases should be first-class citizens.
Chronix leverages Solr and Spark to be storage efficient and to allow interactive
queries for big time series data.
THANK YOU! QUESTIONS?
Mail: [email protected] Twitter: @adersberger
TWITTER.COM/QAWARE - SLIDESHARE.NET/QAWARE
BONUS SLIDES
PERFORMANCE
PREMATURE OPTIMIZATION IS NOT EVIL IF YOU HANDLE BIG DATA
Josef Adersberger
PERFORMANCE
USING A JAVA PROFILER WITH A LOCAL CLUSTER
PERFORMANCE
HIGH-PERFORMANCE, LOW-OVERHEAD COLLECTIONS
PERFORMANCE
830 MB -> 360 MB(- 57%)
unveiled wrong Jackson handling inside of SolrClient
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01THE SECRETS OF DISTRIBUTED PROCESSING PERFORMANCE
Rule 1: Be as close to the data as possible! (CPU cache > memory > local disk > network)
Rule 2: Reduce data volume as early as possible! (as long as you don’t sacrifice parallelization)
Rule 3: Parallelize as much as possible! (max = #cores * x)
PERFORMANCE
THE RULES APPLIED
‣ Rule 1: Be as close to the data as possible! 1. Solr caching2. Spark in-memory processing with activated RDD compression3. Binary protocol between Solr and Spark
‣ Rule 2: Reduce data volume as early as possible! ‣ Efficient storage format (Chronix Format)‣ Predicate pushdown to Solr (query)‣ Group-by & aggregation pushdown to Solr (faceting within a query)
‣ Rule 3: Parallelize as much as possible! ‣ Scale-out on data-level with SolrCloud‣ Scale-out on processing-level with Spark
APACHE SPARK 101
CHRONIX SPARK WONDERLAND
ARCHITECTURE
APACHE SPARK
SPARK TERMINOLOGY (1/2)
▸ RDD: Has transformations and actions. Hides data partitioning & distributed computation. References a set of partitions (“output partitions”) - materialized or not - and has dependencies to another RDD (“input partitions”). RDD operations are evaluated as late as possible (when an action is called). As long as not being the root RDD the partitions of an RDD are in memory but they can be persisted by request.
▸ Partitions: (Logical) chunks of data. Default unit and level of parallelism - inside of a partition everything is a sequential operation on records. Has to fit into memory. Can have different representations (in-memory, on disk, off heap, …)
APACHE SPARK
SPARK TERMINOLOGY (2/2)
▸ Job: A computation job which is launched when an action is called on a RDD.
▸ Task: The atomic unit of work (function). Bound to exactly one partition.
▸ Stage: Set of Task pipelines which can be executed in parallel on one executor.
▸ Shuffling: If partitions need to be transferred between executors. Shuffle write = outbound partition transfer. Shuffle read = inbound partition transfer.
▸ DAG Scheduler: Computes DAG of stages from RDD DAG. Determines the preferred location for each task.
THE COMPETITORS / ALTERNATIVES
CHRONIX RDD VS. SPARK-TS
▸ Spark-TS provides no specific time series storage it uses the Spark persistence mechanisms instead. This leads to a less efficient storage usage and less possibilities to perform performance optimizations via predicate pushdown.
▸ In contrast to Spark-TS Chronix does not align all time series values on one vector of timestamps. This leads to greater flexibility in time series aggregation
▸ Chronix provides multi-dimensional time series as this is very useful for data warehousing and APM.
▸ Chronix has support for Datasets as this will be an important Spark API in the near future. But Chronix currently doesn’t support an IndexedRowMatrix for SparkML.
▸ Chronix is purely written in Java. There is no explicit support for Python and Scala yet.
▸ Chronix doesn not support a ZonedTime as this makes it way more complicated.
CHRONIX SPARK INTERNALS
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01CHRONIXRDD: GET THE CHUNKS FROM SOLR
public ChronixRDD queryChronixChunks( final SolrQuery query, final String zkHost, final String collection, final ChronixSolrCloudStorage<MetricTimeSeries> chronixStorage) throws SolrServerException, IOException { // first get a list of replicas to query for this collection List<String> shards = chronixStorage.getShardList(zkHost, collection); // parallelize the requests to the shards JavaRDD<MetricTimeSeries> docs = jsc.parallelize(shards, shards.size()).flatMap( (FlatMapFunction<String, MetricTimeSeries>) shardUrl -> chronixStorage.streamFromSingleNode( new KassiopeiaSimpleConverter(), shardUrl, query)::iterator); return new ChronixRDD(docs);}
Figure out all Solr shards (using CloudSolrClient in the background)
Query each shard in parallel and convert SolrDocuments to MetricTimeSeries
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01BINARY PROTOCOL WITH STANDARD SOLR CLIENT
private Stream<MetricTimeSeries> streamWithHttpSolrClient(String shardUrl, SolrQuery query, TimeSeriesConverter<MetricTimeSeries> converter) { HttpSolrClient solrClient = getSingleNodeSolrClient(shardUrl); solrClient.setRequestWriter(new BinaryRequestWriter()); query.set("distrib", false); SolrStreamingService<MetricTimeSeries> solrStreamingService = new SolrStreamingService<>(converter, query, solrClient, nrOfDocumentPerBatch); return StreamSupport.stream( Spliterators.spliteratorUnknownSize(solrStreamingService, Spliterator.SIZED), false);}
Use HttpSolrClient pinned to one shard
Use binary (request)protocol
Boilerplate code to stream response
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private Stream<MetricTimeSeries> streamWithCloudSolrStream(String zkHost, String collection, String shardUrl, SolrQuery query, TimeSeriesConverter<MetricTimeSeries> converter) throws IOException { Map params = new HashMap(); params.put("q", query.getQuery()); params.put("sort", "id asc"); params.put("shards", extractShardIdFromShardUrl(shardUrl)); params.put("fl", Schema.DATA + ", " + Schema.ID + ", " + Schema.START + ", " + Schema.END + ", metric, host, measurement, process, ag, group"); params.put("qt", "/export"); params.put("distrib", false); CloudSolrStream solrStream = new CloudSolrStream(zkHost, collection, params); solrStream.open(); SolrTupleStreamingService tupStream = new SolrTupleStreamingService(solrStream, converter); return StreamSupport.stream( Spliterators.spliteratorUnknownSize(tupStream, Spliterator.SIZED), false);}
EXPORT HANDLER PROTOCOL
Pin query to one shard
Use export request handler
Boilerplate code to stream response
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01CHRONIXRDD: FROM CHUNKS TO TIME SERIESpublic ChronixRDD joinChunks() { JavaPairRDD<MetricTimeSeriesKey, Iterable<MetricTimeSeries>> groupRdd = this.groupBy(MetricTimeSeriesKey::new); JavaPairRDD<MetricTimeSeriesKey, MetricTimeSeries> joinedRdd = groupRdd.mapValues((Function<Iterable<MetricTimeSeries>, MetricTimeSeries>) mtsIt -> { MetricTimeSeriesOrdering ordering = new MetricTimeSeriesOrdering(); List<MetricTimeSeries> orderedChunks = ordering.immutableSortedCopy(mtsIt); MetricTimeSeries result = null; for (MetricTimeSeries mts : orderedChunks) { if (result == null) { result = new MetricTimeSeries .Builder(mts.getMetric()) .attributes(mts.attributes()).build(); } result.addAll(mts.getTimestampsAsArray(), mts.getValuesAsArray()); } return result; }); JavaRDD<MetricTimeSeries> resultJavaRdd = joinedRdd.map((Tuple2<MetricTimeSeriesKey, MetricTimeSeries> mtTuple) -> mtTuple._2); return new ChronixRDD(resultJavaRdd); }
group chunks according identity
join chunks tological time series