Time Series Processing with Solr and Spark

A lot of data is best represented as time series: Operational data, financial data, and even in data warehouses the dominant dimension is often time. We present Chronix, a time series database based on Apache Solr and Spark which is able to handle trillions of time series data points and perform interactive queries. Chronix Spark is open source software and battle-proven at a German car manufacturer and an international telco.

We demonstrate several use cases of Chronix from real-life. Afterwards we lift the curtain and deep-dive into the Chronix architecture esp. how we're using Solr to store time series data and how we've hooked up Solr with Spark. We provide some benchmarks showing how Chronix has outperformed other time series databases in both performance and storage-efficiency.

Chronix is open source under the Apache License (http://chronix.io).

Talk on video: https://www.youtube.com/watch?v=UVrOWhMEEf8

Transcript

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
3. TIME SERIES 101
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 set observation @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
12. 12 01 USE CASE: STATISTICS @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
15. CHRONIX SPARK https://github.com/ChronixDB/chronix.spark
17. http://www.datasciencecentral.com
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 Chronix Format Grafana Chronix Analytics Collection Analytics Frontends Logstash fluentd collectd Zeppelin Prometheus Ingestion Bridge KairosDB OpenTSDB InfluxDB 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 MetricObservation DataFrame + 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
31. CHRONIX SPARK INTERNALS
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: seconds nbr 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: josef.adersberger@qaware.de Twitter: @adersberger TWITTER.COM/QAWARE - SLIDESHARE.NET/QAWARE
46. BONUS SLIDES
47. PERFORMANCE
48. codingvoding.tumblr.com
49. PREMATURE OPTIMIZATION IS NOT EVIL IF YOU HANDLE BIG DATA Josef Adersberger
50. PERFORMANCE USING A JAVA PROFILER WITH A LOCAL CLUSTER
51. PERFORMANCE HIGH-PERFORMANCE, LOW-OVERHEAD COLLECTIONS
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
56. CHRONIX SPARK WONDERLAND ARCHITECTURE
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.
60. CHRONIX SPARK INTERNALS
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