barrel: Building a database from scratch

Last year I presented a preliminary version of Barrel, 7 months later the first stable version was available for all. Barrel is a modern document-oriented database with master-master replication targeting micro-services with a RESTful API written in Erlang.

This talk will describe the different challenges we faced in writing a database, from binding C code in Erlang, to write a complete document storage engine with its SQL Engine. This talk will also describe the different patterns used for Reads and Writes concurrency but also continuous automated indexation of the documents.

1. EUC 2017 Stockholm - 06/2017 Building a database 
 from scratch
2. benoît chesneau craftsman working on P2P and custom data endpoints technologies opensource only enki multimedia : the corporate interface about me
3. versatile data endpoint micro-services, message solutions are all based about custom data endpoints need for a simple solution that allows you to bring the data near your service or locally. why barrel?
4. a modern database documents, with time and attachments distributed, local first bring a view of your data near your application automatic indexing focus on simplicity what is barrel?
5. distributed: P2P
6. query a partial view of the data node node
7. local database mobile sensor "cloud" database local database a partial view of the data
8. agnostic indexing
9. barrel can be embedded in your own Erlang application: local database no need to cache platform release: HTTP/Erlang pod to store and query the documents platform
10. problems to solve
11. stateful different queries return different results update expectations read your own write? database complexity
12. processes don’t share anything how do we have multiple writers and multiple readers actor model no integer atomic operations IO operations are “slow” until you get nifs erlang constraints
13. build over existing storage solutions: key/value interface allows atomic batch updates ordered set 1 collection, 1 storage collections are small decisions
14. hierachical dbs db docs a collection multiple collections
 on a node store
15. document: map in erlang revision tree: https://oceanstore.cs.berkeley.edu/publications/ papers/pdf/hh_icdcs03_kang.pdf storing a document
16. revision tree
17. 2 modes: lazy and consistent lazy: indexed asynchronously based on the changes feed consistent support maps, filter, chain opererations based on paths indexing
18. internals
19. using rocksdb for the storage http://gitlab.com/barrel-db/erlang-rocksdb used for memory and disk. optimised for SSD. dirty nifs rocksdb
20. barrel_sup db_sup db db db supervision store
21. writes are queued on the main db process store a canonical version of doc states of the database is shared between other processes via ETS readers are getting last db state via ets write process (current)
22. prevent delayed jobs
23. write more operations at once selective receive group operations based on the document ID (merge) from 40 RPS to 1000 RPS on a node with 4GB of ram and 2 cores) write process (current)
24. By ID, Changes queries get latest DB state from ETS everything happen on the reader process coming: backpressure share the db state across a pool of readers remove the state from ETS readers
25. testing dispatching of write operations on different processes: https://arxiv.org/pdf/1509.07815.pdf testing optimistic writes back pressure: short circuit to not accept more write than the node can sustain based on the running transaction and metrics similar to safety valve:
 https://github.com/jlouis/safetyvalve write process rewrite
26. just appending data to the storage we never read from old index values inside the DB process for consistent write a process listening on db updates events (using a simple gen_server, no gen_event) index policies to index each json segment to retrive via their valur or hash to support value or range queries. indexing process
27. over HTTP cowboy 2 over TCP using teleport and Erlang serialisation (coming): https://gitlab.com/barrel-db/teleport allows embedded mode replication
28. add some instrumentation
29. how to not block without counting first try: statsd client sending to an UDP endpoint counter/gauge/histogram updates we run out of processes & file descriptors asynchronous sending: better. how to make generic? instrumenting
30. add hooks https://github.com/benoitc/hooks prometheus plugin and wombat support (EE version) internal metrics sytem https://gitlab.com/barrel-db/lab/instrument instrumenting
31. roamap
32. 0.9 release: 2017/06/13 https://gitlab.com/barrel-db/barrel-platform add documentation (june 2017) optimise writing atomic updates enrich query engine. roadmap
33. ?
34. twitter: @barreldb web: https://barrel-db.org contact