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Semelhante a Cassandra Data Modeling - Practical Considerations @ Netflix
Semelhante a Cassandra Data Modeling - Practical Considerations @ Netflix (20)
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Cassandra Data Modeling - Practical Considerations @ Netflix
- 1. Cassandra Data modeling Practical considerations Nitish Korla
- 2. Why Cassandra? High Availability / Fully distributed Scalability (Linear) Write performance Simple to install and operate Multi-region replication support (bi-directional)
- 3. Cassandra footprint @ Netflix • 60+ Cassandra clusters • 1600+ nodes holding 100+ TB data • AWS 500 IOPS -> 100, 000 IOPS • Streaming data completely persisted in Cassandra • Related Open Source Projects – Cassandra/Astyanax : in-house committer – Priam : Cassandra Automation – Test Tools : jmeter – http://github.com/netflix
- 4. Data Model keyspace column family Row column • name • value • timestamp Cassandra RDBMS Equivalent KEYSPACE DATABASE/SCHEMA COLUMN FAMILY TABLE ROW ROW FLEXIBLE COLUMNS DEFINED COLUMNS
- 5. Data Model Columns sorted by comparator name 356 Paul group 34567 sex male name 54 kim group 34566 sex female US:CA:Fremont 54353 US:CA:Hayward 34343 status single zip 94538 r o w s Composite columns US:CA:San Jose 987556 population Columns sorted by composite comparators
- 6. Do your Homework ① Understand your application requirements ② Identify your access patterns ③ Model around these access patterns ④ Denormalization is your new friend but… ⑤ Benchmark – Avoid Surprises
- 7. Example 1 : Edge Service
- 8. Edge Services Data Model alloc /xyz/jkl_1 000 active yes script text alloc /xyl/jkl_2 111 active yes script text alloc /xyl/jkl_3 222 active yes script text ROWID ALLOCATION ACTIVE SCRIPT Script_location_version 000 YES OR NO EDGE SERVICE CLUSTER
- 9. Edge Service Anti patterns • High concurrency: Edge servers auto scale • Range scans: Read all data • Large payload: ~1MB of data Very high read latency / unstable cassandra
- 11. Inverted Index considerations • Column name can be used a row key placeholder • Hotspots!! • Sharding
- 12. Other possible improvement • Textual Data • Think compression Upcoming features - Hadoop integration - Solr
- 14. RDBMS -> CASSANDRA user id (primary key) name alias email movie id (primary key) title description user_movie_rating id (primary key) userId (foreign key) movieId (foreign key) rating 1 ∞ 1∞ Queries Get email of userid 123 Get title and description of movieId 222 List all movie names and corresponding ratings for userId 123 List all users and corresponding rating for movieId 222
- 15. CASSANDRA MODEL 123 222:rating 222:title 534:rating 534:title 888:rating 888:title 4 rockstar 2 Finding Nemo 1 Top Guns movieI d userId rating 222 334 455 544 633 789 999 2 5 1 2 2 3 123 name alias email Nitish Korla buckwild nk@netflix.com user 223 title description Find Nemo Good luck with that movie ratingsByMovie ratingsByUser userId Seque nce?
- 16. Example 3 : Viewing History
- 17. Viewing History ROWID 1234454545 : 5466 Format <Timeuuid> : <movieid> 1234454545 : 5466 1234454545 : 5466 1234454545 : 5466 Subscriber_id Playback/Bookmark related SERRIALED DATA Playback/Bookmark related SERRIALED DATA Playback/Bookmark related SERRIALED DATA Playback/Bookmar k related SERRIALED DATA 3454545_5 634534 JSON 3454546_5 JSON 3454547_5 JSON 3454555_9 JSON 3454560_9 JSON 3454580_9 JSON 454545_56 54534 JSON 4454546_5 JSON 4454547_5 JSON 4454555_9 JSON 5554560_9 JSON 5554580_9 JSON 3454545_5 69545 JSON 3454546_5 JSON 3454547_5 JSON 3454555_9 JSON 3454560_9 JSON 3454580_9 JSON 3454545_5 64354 JSON 3454546_5 JSON 3454547_5 JSON 3454555_9 JSON 3454560_9 JSON 3454580_9 JSON
- 18. Viewing History compression ROWID 1234454545_5466 Format <Timeuuid>_<movieid> 1234454546_5466 1234454547_5466 1234454548_5466 Subscriber_id Playback/Bookmark related SERRIALED DATA Playback/Bookmark related SERRIALED DATA Playback/Bookmark related SERRIALED DATA Playback/Bookmark related SERRIALED DATA Re-sort by movie id Movie_id:[{playbackevent1,playbackevent2 ...... } ], Movie_id:[{playbackevent1,playbackevent2 ...... } ], Movie_id:[{playbackevent1,playbackevent2 ...... } ], Movie_id:[{playbackevent1,playbackevent2 ...... } ], Compress data 1 3 2 4 Store in separate column family Reduced data size by 7 times Operational processes improved by 10 times Money saved: $,$$$,$$$Improvement in app read latency
- 19. Think Data Archival • Data stores in Netflix grow exponentially • Have a process in place to archive data – DSE – Moving to a separate column family – Moving to a separate cluster (non SSD) – Setting right expectations w.r.t latencies with historical data • Cassandra TTL’s
- 20. Example 4 : Personalized recommendations
- 21. read-modify-write pattern • Data read and written back (even if data was not modified) • Large BLOB’s Cassandra under IO pressure Peak traffic – compaction yet to run – high read latency
- 22. read-modify-write pattern • Do you really need to read data ? • Avoid write if data has not changed – SSTable creation – immutable SSTables created at backend • Write with a new row key (Limit sstable scans). TTL data • If a batch process, throttle the write rate to let compactions catch up
- 23. Useful Tools • Cassandra real-time metrics • Capture schema changes –(automatically)
- 24. Observations • Cassandra scales linearly without any noticeable degradation to running cluster • Self-healing : minimal operational noise • Developers – mindset need to shift from normalization to denormalization – Need to have reasonable understanding of Cassandra architecture – Enjoy the schema change flexibility. No more DDL locks/ DBA dependency
- 25. Questions
- 26. Reading from Cassandra client memtable sstable sstable sstable Row cache key cache
- 27. Writing to Cassandra client Commit log (Disk) Memtable (memory) sstable Flush Replication factor: 3 sstable sstablesstable
Notas do Editor
- Start with some live example.. And then use it as segway to cover some best practices
- RdbmsbackgroudKeyspace -> DBCF -> TableRow groups columnsEach column is a tripletColumn naming is not necessary/could be different. Column comparator specifies the sorting.. No need to stick to certain rules Name -> sortedTimestamp -> conflict resolution
- Rows are indexedColumns are sorted based on comparator you specify, so use it to your benefitKeep column names short as they are repeated Column size = 15 bytes + size of name + size of value Don’t store empty columns if there is no need – schema free designCOMPOSITE COLUMNScustom inverted search indexes: when you want more control over the CF layout than a secondary indexa replacement for super columns: both and a means to offset some of the worst performance penalties associated with such, as well as extend the model to provide and arbitrary level of nestinggrouping otherwise static skinny rows into wider rows for greater efficiency
- Cassandra is for point queriesStill ok for small set of rows
- API servers autoscale or new push, they need to read majority of rows in scripts column family
- Simple but powerful concept – based on premise thatrows are indexed and point looks are fasterCreate another column family and store list of all required rowid’s for faster lookup
- Wide row can reside only on one node.. And that can create hot spotsSharding – application logic / buckets
- 20% performance loss due to parsing1.2netty protocol
- Start with some live example.. And then use it as segway to cover some best practices
- One to one mapping doesn’t workFifth normal form deals with cases where information can be reconstructed from smaller pieces of information that can be maintained with less redundancy. Second, third, and fourth normal forms also serve this purpose, but fifth normal form generalizes to cases not covered by the others. - multi-valued depedencies
- Sequence in cassandra??Index lookupdenormalization
- We don’t have linear growthTTL fascinating feature… coming from oracle backgroundViewing history dataWide row implementation, Compressed dataStored till perpetuitySome rows have ~20M of data (and growing)App code paginates through columns - Good thingCapacity considerationCassandra house keeping (more data -> repairs/bootstraps)
- We don’t have linear growthTTL fascinating feature… coming from oracle backgroundViewing history dataWide row implementation, Compressed dataStored till perpetuitySome rows have ~20M of data (and growing)App code paginates through columns - Good thingCapacity considerationCassandra house keeping (more data -> repairs/bootstraps)
- We don’t have linear growthTTL fascinating feature… coming from oracle background
- Read is going to drive the latency of overall request
- architecture to reap the benefits of distributed computing / high performance
- 2 digest query/ 1 complete data response. The optimization is only on the bandwidthNumber of replicas contacted depend on the consistency level specifiedHinted handoff, read repair, antientropy node repairDon’t expect cassandra as a load balancer
- Commit log for durability – sequential writeMemtable – no disk access (no reads or seeks)Sstables written sequentially to the diskThe operational design integrates nicely with the operating system page cache. Because Cassandra does not modify the data, dirty pages that would have to be flushed are not even generated.