Time Series Processing with Solr and Spark
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The document elaborates on leveraging Solr and Spark for efficient time series data storage and processing, addressing various operational and analytical use cases. It explains the Chronix stack, including its architecture, APIs, and best practices for optimizing performance with distributed systems. The content emphasizes the importance of performance tuning, data retention, and efficient query handling in managing large-scale time series data.
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CHRONIX 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
@adersberger](https://image.slidesharecdn.com/adersberger-timeseries-analysis-161014172523/85/Time-Series-Processing-with-Solr-and-Spark-33-320.jpg)
Time Series Processing with Solr and Spark
- 1. O C T O B E R 1 1 - 1 4 , 2 0 1 6 • B O S T O N , M A
- 2. Time Series Processing with Solr and Spark Josef Adersberger (@adersberger) CTO, QAware
- 4. 4 01 WE’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
- 5. 5 WE’RE SURROUNDED BY TIME SERIES (Pt. 2) ▸ Oktoberfest: Visitor and beer consumption trend the singularity
- 6. 6 01 TIME SERIES: BASIC TERMS univariate time series multivariate time series multi-dimensional time series (time series tensor) time series setobservation @adersberger
- 7. 7 01 ILLUSTRATIVE OPERATIONS ON TIME SERIES align Time series => Time series diff downsampling outlier min/max avg/med slope std-dev Time series => Scalar @adersberger
- 8. OUR USE CASE
- 9. Monitoring Data Analysis of a business-critical, worldwide distributed software system. Enable root 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.
- 11. 11 01 USE CASE: EXPLORING Drill-down host process measurements counters (metrics) Query time series metadata Superimpose time series @adersberger
- 13. 13 01 USE CASE: ANOMALY DETECTION Featuring Twitter Anomaly Detection (https://github.com/twitter/AnomalyDetection and Yahoo EGDAS https://github.com/yahoo/egads @adersberger
- 14. 14 01 USE CASE: SQL AND ZEPPELIN @adersberger
- 19. 19 01 AVAILABLE TIME SERIES DATABASES https://github.com/qaware/big-data-landscape
- 20. EASY-TO-USE BIG TIME SERIES DATA STORAGE & PROCESSING ON SPARK
- 21. 21 01 THE 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 ChronixFormat GrafanaChronix Analytics Collection Analytics Frontends Logstash fluentd collectd Zeppelin Prometheus Ingestion Bridge KairosDB OpenTSDBInfluxDB Graphite
- 22. 22 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) @adersberger
- 23. 23 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”) @adersberger
- 24. 24 01 CHRONIX 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) @adersberger
- 25. 25 01 CHRONIX SPARK API: DATA MODEL MetricTimeSeries MetricObservationDataFrame + toDataFrame() @adersberger Dataset<MetricTimeSeries> Dataset<MetricObservation> + toDataset() + toObservationsDataset() ChronixRDD
- 26. 26 01 SPARK APIs FOR DATA PROCESSING RDD DataFrame Dataset typed yes no yes optimized medium highly highly mature yes yes medium SQL no yes no @adersberger
- 27. 27 01 CHRONIX RDD Statistical operations the set characteristic: a JavaRDD of MetricTimeSeries Filter the set (esp. by dimensions) @adersberger
- 28. 28 01 METRICTIMESERIES DATA TYPE access all timestamps the multi-dimensionality: get/set dimensions (attributes) access all observations as stream access all numeric values @adersberger
- 30. 30 01 //Create Chronix Spark context from a SparkContext / JavaSparkContext ChronixSparkContext csc = new ChronixSparkContext(sc); //Read data into ChronixRDD SolrQuery 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 RDD double 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(); @adersberger
- 32. 32 Distributed Data & Data Retrieval ‣ Data sharding (OK) ‣ Fast index-based queries (OK) ‣ Efficient storage format @adersberger
- 33. 33 01 CHRONIX 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 @adersberger
- 34. 34 01 CHRONIX 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) @adersberger
- 35. 35 01 CHRONIX FORMAT: TUNING CHUNK SIZE AND CODEC GZIP + 128 kBytes Florian Lautenschlager, Michael Philippsen, Andreas Kumlehn, Josef Adersberger Chronix: Efficient Storage and Query of Operational Time Series International Conference on Software Maintenance and Evolution 2016 (submitted) @adersberger storage demand access time
- 36. 36 01 CHRONIX FORMAT: STORAGE EFFICIENCY BENCHMARK @adersberger
- 37. 37 01 CHRONIX FORMAT: PERFORMANCE BENCHMARK unit: secondsnbr of queries query @adersberger
- 38. 38 Distributed Processing ‣ Heavy lifting distributed processing ‣ Efficient integration of Spark and Solr @adersberger
- 39. 39 01 SPARK 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) @adersberger
- 40. 40 01 ALIGN 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 @adersberger
- 41. 41 01 SPARK AND SOLR BEST PRACTICES: PUSHDOWN SolrQuery 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
- 42. 42 01 SPARK 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 Chronix RDD CloudSolr Stream Format Decoder bulk of JSON tuples Chronix Spark Solr / SolrJ
- 43. 43 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
- 44. 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.
- 45. THANK YOU! QUESTIONS? Mail: [email protected] Twitter: @adersberger TWITTER.COM/QAWARE - SLIDESHARE.NET/QAWARE
- 46. BONUS SLIDES
- 47. PERFORMANCE
- 49. PREMATURE OPTIMIZATION IS NOT EVIL IF YOU HANDLE BIG DATA Josef Adersberger
- 50. PERFORMANCE USING A JAVA PROFILER WITH A LOCAL CLUSTER
- 52. PERFORMANCE 830 MB -> 360 MB (- 57%) unveiled wrong Jackson handling inside of SolrClient
- 53. 53 01 THE 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)
- 54. PERFORMANCE THE RULES APPLIED ‣ Rule 1: Be as close to the data as possible! 1. Solr caching 2. Spark in-memory processing with activated RDD compression 3. 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
- 55. APACHE SPARK 101
- 57. 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, …)
- 58. 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.
- 59. 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.
- 61. 61 01 CHRONIXRDD: 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
- 62. 62 01 BINARY 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
- 63. 63 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
- 64. 64 01 CHRONIXRDD: FROM CHUNKS TO TIME SERIES public 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 to logical time series