Time-oriented event search. A new level of scale
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Time-oriented event search. A new level of scale
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Time-oriented event search. A new level of scale
- 1. © Rocana, Inc. All Rights Reserved. | 1 Joey Echeverria, Platform Technical Lead - @fwiffo Michael Peterson, Platform Engineer - @quux00 Hadoop Summit Ireland 2016 Time-oriented event search A new level of scale
- 2. © Rocana, Inc. All Rights Reserved. | 2 Context • We built 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 of data 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 Search 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 Key Goals for Rocana Search • Higher indexing throughput per node than Solr for time-oriented event data • Scale horizontally better than Solr • Support an arbitrary number of dynamically created partitions • Arbitrarily large amounts of indexed data on disk • all data queryable without wasting resources for infrequently used data • Ability to add/remove Search nodes dynamically without any manual restarts or rebalances
- 10. © Rocana, Inc. All Rights Reserved. | 10 Some Key Features of Rocana Search • Fully parallelized ingest and query, built for large clusters • Every node is an indexer Hadoop Node Rocana Search Hadoop Node Rocana SearchHadoop Node Rocana Search Hadoop Node Rocana Search Kafka
- 11. © Rocana, Inc. All Rights Reserved. | 11 Some Key Features of Rocana Search • Every node is a query coordinator and executor Query Client Rocana Search Coord Exec Rocana Search Coord Exec Rocana Search Coord Exec Rocana Search Coord Exec
- 12. © Rocana, Inc. All Rights Reserved. | 12 Architecture (A single node) RS HDFS MetadataIndex Management Coordinator ExecutorLucene Indexes Query Client Kafka Data Producers ZK
- 13. © Rocana, Inc. All Rights Reserved. | 13 Sharding Model: datasets, partitions, and slices • A search dataset is split into partitions by a partition strategy • Think: “By year, month, day” • 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 day has 20 slices, each of which is independent for write” • Number of slices == Kafka partitions
- 14. © Rocana, Inc. All Rights Reserved. | 14 Datasets, partitions, and slices Dataset “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
- 15. © Rocana, Inc. All Rights Reserved. | 15 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
- 16. © Rocana, Inc. All Rights Reserved. | 16 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
- 17. © Rocana, Inc. All Rights Reserved. | 17 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
- 18. © Rocana, Inc. All Rights Reserved. | 18 Query • Queries submitted to coordinator via RPC • Coordinator parses query and aggressively prunes partitions to search by analyzing predicates • Coordinator schedules and monitors fragments, merges results, responds to client • Fragments are submitted to executors for processing • Executors search exactly what they’re told, stream to coordinator • Fragment is generated for every slice that may contain data
- 19. © Rocana, Inc. All Rights Reserved. | 19 Some benefits of the design • Search processes are on the same nodes as the HDFS DataNode • First replica of any event received by search from Kafka is written locally • Unless nodes fail, all reads are local (HDFS short circuit reads) • Linux kernel page cache is useful here • HDFS caching could also be used (not yet doing this) • Search uses 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
- 20. © Rocana, Inc. All Rights Reserved. | 20 Initial Benchmarks: Event ingest and indexing • Early days on this . . . • Most recent data we have for Rocana Search vs. Solr • using CMS GC • on Hadoop/HDFS (CDH) • on AWS (d2.2xlarge) – 8 cpus, 60 GB RAM • 4 nodes, 8 shards • 12 hour run, ~300 GiB indexed to disk • Solr = 11,000 events/sec • Rocana Search = 36,500 events/sec
- 21. © Rocana, Inc. All Rights Reserved. | 21 Initial Benchmarks: Query During Ingest
- 22. © Rocana, Inc. All Rights Reserved. | 22 Initial Benchmarks: Query (No Ingest)
- 23. © Rocana, Inc. All Rights Reserved. | 23 What we’ve really shown In the context of search, scale means: • High cardinality: Billions of unique events per day • High speed ingest: Hundreds of thousands of events per second • Not having to age data out of the dataset • Handling large, concurrent queries, while ingesting data • Fully utilizing modern hardware These things are very possible
- 24. © Rocana, Inc. All Rights Reserved. | 24 Thank you! Questions? joey@rocana.com mpeterson@rocana.com The Rocana Search Team: • Michael Peterson - @quux00 • Mark Tozzi - @not_napoleon • Brad Cupit - @bradcupit • Brett Hoerner - @bretthoerner • Joey Echeverria - @fwiffo • Eric Sammer - @esammer
Editor's Notes
- 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.