High cardinality time series search: A new level of scale - Data Day Texas 2016
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Modern search systems provide incredible feature sets, developer-friendly APIs, and low latency indexing and query response. By some measures, these systems operate "at scale," but rarely is that quantified. Customers of Rocana typically look to push ingest rates in excess of 1 million events per second, retaining years of data online for query, with the expectation of sub-second response times for any reasonably sized subset of data. We quickly found that the tradeoffs made by general purpose search systems, while right for common use cases, were less appropriate for these high cardinality, large scale use cases. This session details the architecture, tradeoffs, and interesting implementation decisions made in building a new time series optimized distributed search system using Apache Lucene, Kafka, and HDFS. Data ingestion and durability, index and metadata organization, storage, query scheduling and optimization, and failure modes will be covered. Finally, a summary of the results achieved will be shown.
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High cardinality time series search: A new level of scale - Data Day Texas 2016
- 1. © Rocana, Inc. All Rights Reserved. | 1 Eric Sammer – CTO and co-founder, @esammer Data Day Texas 2016 High cardinality time series search A new level of scale
- 2. © Rocana, Inc. All Rights Reserved. | 2 Context • We build a system for large scale realtime collection, processing, and analysis of event-oriented machine data • On prem or in the cloud, but not SaaS • Supportability is a big deal for us • Predictability of performance and under failures • Ease of configuration and operation • Behavior in wacky environments • All of our decisions are informed by this - YMMV
- 3. © Rocana, Inc. All Rights Reserved. | 3 What I mean by “scale” • Typical: 10s of TB of new data per day • Average event size ~200-500 bytes • 20TB per day • @200 bytes = 1.2M events / second, ~109.9B events / day, 40.1T events / year • @500 bytes = 509K events / second, ~43.9B events / day, 16T events / year, • Retaining years online for query
- 4. © Rocana, Inc. All Rights Reserved. | 4 General purpose search – the good parts • We originally built against Solr Cloud (but most of this goes for Elastic Seach too) • Amazing feature set for general purpose search • Good support for moderate scale • Excellent at • Content search – news sites, document repositories • Finite size datasets – product catalogs, job postings, things you prune • Low(er) cardinality datasets that (mostly) fit in memory
- 5. © Rocana, Inc. All Rights Reserved. | 5 Problems with general purpose search systems • Fixed shard allocation models – always N partitions • Multi-level and semantic partitioning is painful without building your own macro query planner • All shards open all the time; poor resource control for high retention • APIs are record-at-a-time focused for NRT indexing; poor ingest performance (aka: please stop making everything REST!) • Ingest concurrency is wonky • High write amplification on data we know won’t change • Other smaller stuff…
- 6. © Rocana, Inc. All Rights Reserved. | 6 “Well actually…” Plenty of ways to push general purpose systems (We tried many of them) • Using multiple collections as partitions, macro query planning • Running multiple JVMs per node for better utilization • Pushing historical searches into another system • Building weirdo caches of things At some point the cost of hacking outweighed the cost of building
- 7. © Rocana, Inc. All Rights Reserved. | 7 Warning! • This is not a condemnation of general purpose search systems! • Unless the sky is falling, use one of those systems
- 8. © Rocana, Inc. All Rights Reserved. | 8 We built a thing: Rocana Search High cardinality, low latency, parallel search system for time-oriented events
- 9. © Rocana, Inc. All Rights Reserved. | 9 Features of Rocana Search • Fully parallelized ingest and query, built for large clusters • Every node is an indexer, query coordinator, and executor • Optimized for high cardinality time-oriented event data • Built to keep all data online and queryable without wasting resources for infrequently used data • Fully durable, resistant to node failures • Operationally friendly: online ops, predictable resource usage and performance • Uses battle tested open source components (Kafka, Lucene, HDFS, ZK)
- 10. © Rocana, Inc. All Rights Reserved. | 10 Major differences • Storage and partition model looks more like range-partitioned tables in databases; new partitions easily added, old ones dropped, support for multi-field partitioning • Partitions subdivided into slices for parallel writes • Query engine aggressively prunes partitions by analyzing predicates • Ingestion path is Kafka, built for extremely high throughput of small events What we know about our data allows us to optimize
- 11. © Rocana, Inc. All Rights Reserved. | 11 Architecture (A single node) RS HDFS MetadataIndex Management Coordinator ExecutorIndexes Query Client Kafka Data Producers ZK
- 12. © Rocana, Inc. All Rights Reserved. | 12 Collections, partitions, and slices • A search collection is split into partitions by a partition strategy • Think: “By year, month, day, hour” • Partitioning invisible to queries (e.g. `time:[x TO y] AND host:z` works normally) • Partitions are divided into slices to support lock-free parallel writes • Think: “This hour has 20 slices, each of which is independent for write”
- 13. © Rocana, Inc. All Rights Reserved. | 13 Collections, partitions, and slices Collection “events” Partition “2016/01/01” Slice 0 Slice 1 Slice 2 Slice N Partition “2016/01/02” Slice 0 Slice 1 Slice 2 Slice N
- 14. © Rocana, Inc. All Rights Reserved. | 14 From events to partitions to slices Partition 2016/01/01 Slice 0 Slice 1 Topic events KP 0 KP 1 Event 1 2016/01/01 Event 2 2016/01/01 Event 3 2016/01/02 Partition 2016/01/02 Slice 0 Slice 1 E1 E2 E3
- 15. © Rocana, Inc. All Rights Reserved. | 15 Assigning slices to nodes Node 1 Partition 2016/01/01 S 0 S 2 Partition 2016/01/02 S 0 S 2 Partition 2016/01/03 S 0 S 2 Partition 2016/01/04 S 0 S 2 Topic eventsKP 0 KP 2 KP 1 KP 3 Node 2 Partition 2016/01/01 S 1 S 3 Partition 2016/01/02 S 1 S 3 Partition 2016/01/03 S 1 S 3 Partition 2016/01/04 S 1 S 3
- 16. © Rocana, Inc. All Rights Reserved. | 16 Following the write path • One of the search nodes is the exclusive owner of KP 0 and KP 1 • Consume a batch of events • Use the partition strategy to figure out to which RS partition it belongs • Kafka messages carry the partition so we know the slice • Event written to the proper partition/slice • Eventually the indexes are committed • If the partition or slice is new, metadata service is informed
- 17. © Rocana, Inc. All Rights Reserved. | 17 Query engine basics • Queries submitted to coordinator via RPC • Coordinator (smart) parses, plans, schedules and monitors fragments, merges results, responds to client • Fragments are submitted to executors for processing • Executors (dumb) search exactly what they’re told, stream to coordinator • Fragment is generated for every partition/slice that may contain data
- 18. © Rocana, Inc. All Rights Reserved. | 18 Some implications • Search processes are on the same nodes as the HDFS DataNode • First replica of any event received by search from Kafka is written locally • Result: Unless nodes fail, all reads are local (HDFS short circuit reads) • Linux kernel page cache is useful here • HDFS caching can be used • Search has an off-heap block cache as well • In case of failure, any search node can read any index • HDFS overhead winds up being very little, still get the advantages
- 19. © Rocana, Inc. All Rights Reserved. | 19 Contrived query scenario • 80 Kafka partitions (80 slices) • Collection partitioned by day • 80 nodes, 16 executor threads each • Query: time:[2015-01-01 TO 2016-01-01] AND service:sshd • 365 * 80 = 29200 fragments generated for the query (a lot!) • 29200 / (80 * 16) = ~22 “waves” of fragments • If each “wave” takes ~0.5 second, the query takes ~11 seconds
- 20. © Rocana, Inc. All Rights Reserved. | 20 More real, but preliminary • 24 AWS EC2 d2.2xl, instance storage • Ingesting data at ~3 million events per minute (50K eps) • 24 Kafka partitions / RS slices • Index size: 5.9 billion events • Query: All events, facet by 3 fields • No tuning (default config): ~10 seconds (with a silly bug) • 10 concurrent instances of the same query: ~21 seconds total • 50 concurrent instances: ~41 seconds • We will do much better shortly (*ahem*, Brett)!
- 21. © Rocana, Inc. All Rights Reserved. | 21 What we’ve really shown In the context of search, scale means: • High cardinality: Billions of events per day • High speed ingest: Hundreds of thousands of events per second • Not having to age data out of the collection • Handling large, concurrent queries, while ingesting data • Fully utilizing modern hardware These things are very possible
- 22. © Rocana, Inc. All Rights Reserved. | 22 Next steps • Read replicas • Smarter partition elimination in complex queries • Speculative execution of query fragments • Additional metadata for index fields to improve storage efficiency • Smarter cache management • Better visibility into performance and health • Strong consensus (e.g. Raft, multi-paxos) for metadata?
- 23. © Rocana, Inc. All Rights Reserved. | 23 Thank you! Hopefully I still have time for questions. rocana.com @esammer esammer@rocana.com (ask me for stickers) The (amazing) core search team: • Brett Hoerner - @bretthoerner • Michael Peterson - @quux00 • Mark Tozzi - @not_napoleon
Notes de l'éditeur
- YMMV Not necessarily true for you Enterprise software – shipping stuff to people Fine grained events – logs, user behavior, etc. For everything – solving the problem of “enterprise wide” ops, so it’s everything from everywhere from everyone for all time (until they run out of money for nodes). This isn’t condemnation of general purpose search engines as much as what we had to do for our domain
- YMMV Not necessarily true for you Enterprise software – shipping stuff to people Fine grained events – logs, user behavior, etc. For everything – solving the problem of “enterprise wide” ops, so it’s everything from everywhere from everyone for all time (until they run out of money for nodes). This isn’t condemnation of general purpose search engines as much as what we had to do for our domain
- It does most of what you want for most cases most of the time. They’ve solved some really hard problems. Content search (e.g. news sites, document repos), finite size datasets (e.g. product catalogs), low cardinality datasets that fit in memory. Not us.
- Flexible systems with a bevy of full text search features Moderate and fixed document count: big by historical standards, small by ours. Design reflects these assumptions. Fixed sharding at index creation. Partition events into N buckets. For long retention time-based systems, this isn’t how we think. Let’s keep it until it’s painful. Then we add boxes. When that’s painful, we prune. Not sure what that looks like. Repartitioning is not feasible at scale. Partitions count should be dynamic. Multi-level partitioning is painful without building your own query layer; by range(time), then hash(region) or identity(region). All shards are open all the time. Implicit assumption that either you 1. have queries that touch the data evenly or 2. have inifinite resources. Recent time events are hotter than distant, but distant still needs to be available for query. Poor cache control. Recent data should be in cache. Historical scans shouldn’t push recent data out of cache. APIs are extremely “single record” focused. REST with record-at-a-time is absolutely abysmal for high throughput systems. Batch indexing is not useful. No in between. Read replicas are expensive and homogenous. Ideally we have 3 read replicas for the last N days and 1 for others. Replicas (for performance) should take up space in memory, but not on disk. Ingest concurrency tends to be wonky; whole lotta locking going on. Anecdotally, it’s difficult to get Solr Cloud to light up all cores on a box without running multiple JVMs; something is weird. We can get the benefits of NRT indexing speed with fewer writer checkpoints because our ingest pipeline acts as a reliable log. We recover from Kafka based on the last time the writer checkpointed so we can checkpoint very infrequently if we want. We know our data doesn’t change, or changes very little, after a certain point, so we can optimize and freeze indexes reducing write amplification from compactions.
- There are plenty of ways we could of pushed the general purpose systems, and we did. We layered our own partitioning and shard selection on top of Solr Cloud with time-based collection round robining. That got us pretty far, but not far enough. We were starting to do a lot of query rewriting and scheduling. Run mulitple JVMs per box. Gross. Unsupportable. Push historical queries out of search to a system such as spark. Build weird caches of frequent data sets. At some point, the cost of hacking outweighed the cost of building.
- Flexible systems with a bevy of full text search features Moderate and fixed document count: big by historical standards, small by ours. Design reflects these assumptions. Fixed sharding at index creation. Partition events into N buckets. For long retention time-based systems, this isn’t how we think. Let’s keep it until it’s painful. Then we add boxes. When that’s painful, we prune. Not sure what that looks like. Repartitioning is not feasible at scale. Partitions count should be dynamic. Multi-level partitioning is painful without building your own query layer; by range(time), then hash(region) or identity(region). All shards are open all the time. Implicit assumption that either you 1. have queries that touch the data evenly or 2. have inifinite resources. Recent time events are hotter than distant, but distant still needs to be available for query. Poor cache control. Recent data should be in cache. Historical scans shouldn’t push recent data out of cache. APIs are extremely “single record” focused. REST with record-at-a-time is absolutely abysmal for high throughput systems. Batch indexing is not useful. No in between. Read replicas are expensive and homogenous. Ideally we have 3 read replicas for the last N days and 1 for others. Replicas (for performance) should take up space in memory, but not on disk. Ingest concurrency tends to be wonky; whole lotta locking going on. Anecdotally, it’s difficult to get Solr Cloud to light up all cores on a box without running multiple JVMs; something is weird. We can get the benefits of NRT indexing speed with fewer writer checkpoints because our ingest pipeline acts as a reliable log. We recover from Kafka based on the last time the writer checkpointed so we can checkpoint very infrequently if we want. We know our data doesn’t change, or changes very little, after a certain point, so we can optimize and freeze indexes reducing write amplification from compactions.