The Future of Distributed Databases is Relational | QCon London 2018 | Sumedh Pathak

The Future of Distributed Databases is Relational | QCon London 2018 | Sumedh Pathak

Years ago when working at Amazon on shopping cart infrastructure and the precursor to DynamoDB, my co-founder and I realized that while distributed key value stores were useful for a few use-cases, we missed many of the benefits of relational databases: transactions, joins, and the power of the lingua franca of RDBMS’s: SQL. So we challenged ourselves to modernize the traditional relational database, to take a robust open source relational database and transform it into a distributed database.

This talk is about my team’s journey to create a more modern relational database. I’ll talk about the distributed systems problems we had to solve in order to scale out the Postgres open source database, in order to achieve parallelism and a concomitant increase in performance. I'll describe the architecture of the distributed query planner; how we extend traditional relational algebra operators to plan distributed queries and scale reads. I’ll also describe distributed deadlock detection, and how that enabled us to scale out transactions spanning multiple machines.

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Citus Data

March 06, 2018
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  1. Sumedh Pathak, Co-Founder & VP Engineering, Citus Data QCon London

    2018 The Future of Distributed Databases is Relational @moss_toss | @citusdata
  2. About Me • Co-Founder & VP Engineering at Citus Data

    • Amazon Shopping Cart (former) • Amazon Supply Chain & Order Fulfillment (former) • Stanford Computer Science Citus Data co-founders, left-to-right Ozgun Erdogan, Sumedh Pathak, & Umur Cubukcu Photo cred: Willy Johnson, Monterey CA, Sep 2017 Sumedh Pathak | Citus Data | QCon London 2018
  3. None
  4. Why RDBMS? Sumedh Pathak | Citus Data | QCon London

    2018
  5. Streaming Storage Map Reduce NoSQL SQL Database SELECT ... Application

    Application Sumedh Pathak | Citus Data | QCon London 2018 Because your architecture could be simpler
  6. An RDBMS is a general-purpose data platform Sumedh Pathak |

    Citus Data | QCon London 2018 Fast writes Real-time & bulk High throughput High concurrency Data consistency Query optimizers
  7. PostgreSQL MySQL MongoDB SQL Server + Oracle Source: Hacker News,

    https://news.ycombinator.com Startups Are Choosing Postgres % database job posts mentioning each database, across 20K+ job posts Sumedh Pathak | Citus Data | QCon London 2018
  8. but RDBMS’s don’t scale, right? Sumedh Pathak | Citus Data

    | QCon London 2018
  9. but RDBMS’s don’t scale RDBMS’s are hard to scale Sumedh

    Pathak | Citus Data | QCon London 2018
  10. What exactly needs to Scale? - Tables (Data) - Partitioning,

    Co-location, Reference Tables - SQL (Reads) - How do we express and optimize distributed SQL - Transactions (Writes) - Cross Shard updates/deletes, Global Atomic Transactions Sumedh Pathak | Citus Data | QCon London 2018 1 2 3
  11. Scaling Tables Sumedh Pathak | Citus Data | QCon London

    2018
  12. How to partition the data? - Pick a column -

    Date - Id (customer_id, cart_id) - Pick a method - Hash - Range
  13. Partition data across nodes R 1 R 2 R 3

    R 4 R 5 R 6 R 7 Coordinator Node Worker Nodes Shards Sumedh Pathak | Citus Data | QCon London 2018
  14. Worker → RDBMS, Shard → Table

  15. Reference Tables N 1 N 1 N 1 N 1

    Copies of same table Sumedh Pathak | Citus Data | QCon London 2018 Coordinator Node Worker Nodes
  16. Co-Location R 1 R 2 R 3 R 4 S

    1 S 2 S 3 S 4 Explicit Co-Location API. E.g. Partition by Tenant Sumedh Pathak | Citus Data | QCon London 2018 Coordinator Node Worker Nodes
  17. What about Foreign Keys?

  18. The key to scaling tables... - Use relational databases as

    a building block - Understand semantics of application—to be smart about partitioning - Multi-tenant applications
  19. Scaling SQL

  20. FROM table R SELECT x Project x (R) → R’

    WHERE f(x) Filter f(x) (R) → R’ … JOIN … R × S → R’ SQL ↔ Relational Algebra Sumedh Pathak | Citus Data | QCon London 2018
  21. FROM sharded_table Collect(R 1 ,R 2 ,...) → R Distributed

    Relational Algebra R 1 R 2 C Sumedh Pathak | Citus Data | QCon London 2018
  22. Project x (Collect(R 1 ,R 2 ,...)) = Collect(Project x

    (R 1 ), Project x (R 2 )...) Commutative property R 1 R 2 C R 1 R 2 C P x P x P x Sumedh Pathak | Citus Data | QCon London 2018
  23. Collect(R 1 ,R 2 ,...) x Collect(S 1 ,S 2

    ,...) = Collect(R 1 × S 1 ,R 2 × S 2, ...) Distributive property R 1 R 2 C × C S 1 S 2 R 1 R 2 C × S 1 S 2 × X = Join Operator
  24. SUM(x)(Collect(R 1 ,R 2 ,...)) = SUM(Collect(SUM(R 1 ), SUM(R

    2 )...)) Associative property R 1 R 2 C R 1 R 2 C Sum x Sum x Sum x Sumedh Pathak | Citus Data | QCon London 2018 Sum x
  25. SELECT sum(price) FROM orders, nation WHERE orders.nation = nation.name AND

    orders.date >= '2012-01-01' AND nation.region = 'Asia'; Sumedh Pathak | Citus Data | QCon London 2018
  26. Sumedh Pathak | Citus Data | QCon London 2018

  27. Sumedh Pathak | Citus Data | QCon London 2018 Volcano

    style processing Data flows from bottom to top
  28. Sumedh Pathak | Citus Data | QCon London 2018

  29. Sumedh Pathak | Citus Data | QCon London 2018

  30. Sumedh Pathak | Citus Data | QCon London 2018 Parallelize

    Aggregate Push Joins & Filters below collect. Run in parallel across all nodes Filters & Projections done before Join
  31. SELECT sum(intermediate_col) FROM <concatenated results>; SELECT sum(price) FROM orders_2 JOIN

    nation_2 ON (orders_2.name = nation_2.name) WHERE orders_2.date >= '2017-01-01' AND nation_2.region = 'Asia'; SELECT sum(price) FROM orders_2 JOIN nation_1 ON (orders_2.name = nation_1.name) WHERE orders_2.date >= '2017-01-01' AND nation_2.region = 'Asia'; Sumedh Pathak | Citus Data | QCon London 2018
  32. Executing Distributed SQL SQL database orders_1 nation_1 orders nation SELECT

    sum(price) FROM orders_2 o JOIN nation_1 ON (o.name = n.name) WHERE o.date >= '2017-01-01' AND n.region = 'Asia'; SELECT sum(price) FROM orders_1 o JOIN nation_1 ON (o.name = n.name) WHERE o.date >= '2017-01-01' AND n.region = 'Asia'; SELECT sum(price) FROM <results>; SQL database orders_2 nation_1
  33. The key to scaling SQL... - New relational algebra operators

    for distributed processing - Relational Algebra Properties to optimize tree: Commutativity, Associativity, & Distributivity - Map / Reduce operators
  34. Scaling Transactions

  35. Money Transfer, as an example BEGIN; UPDATE accounts SET balance

    = balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; Sumedh Pathak | Citus Data | QCon London 2018
  36. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 Coordinator Sumedh Pathak | Citus Data | QCon London 2018
  37. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 BEGIN; UPDATE accounts SET balance = balance - 100 WHERE id = ‘ALICE’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  38. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 BEGIN; UPDATE accounts SET balance = balance - 100 WHERE id = ‘ALICE’; COMMIT; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  39. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 Coordinator BEGIN; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; Sumedh Pathak | Citus Data | QCon London 2018 BEGIN; UPDATE accounts SET balance = balance - 100 WHERE id = ‘ALICE’;
  40. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 BEGIN; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT Sumedh Pathak | Citus Data | QCon London 2018 BEGIN; UPDATE accounts SET balance = balance - 100 WHERE id = ‘ALICE’; Coordinator
  41. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 PREPARE TRANSACTION ‘citus_...98’; PREPARE TRANSACTION ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  42. What happens during PREPARE? State of transaction stored on a

    durable store Locks are maintained &
  43. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 Coordinator PREPARE TRANSACTION ‘citus_...98’; ROLLBACK TRANSACTION ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018
  44. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 PREPARE TRANSACTION ‘citus_...98’; PREPARE TRANSACTION ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  45. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 COMMIT PREPARED ‘citus_...98’; COMMIT PREPARED ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  46. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 COMMIT PREPARED ‘citus_...98’; COMMIT PREPARED ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  47. A 1 A 2 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; COMMIT; A 3 A 4 COMMIT PREPARED ‘citus_...98’; COMMIT PREPARED ‘citus_...98’; Sumedh Pathak | Citus Data | QCon London 2018 Coordinator
  48. But.... Deadlocks!

  49. Example // SESSION 1 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; (LOCK on ROW with id ‘ALICE’) // SESSION 2 Sumedh Pathak | Citus Data | QCon London 2018
  50. Example // SESSION 1 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; (LOCK on ROW with id ‘ALICE’) // SESSION 2 BEGIN; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; (LOCK on ROW with id ‘BOB’) Sumedh Pathak | Citus Data | QCon London 2018
  51. Example // SESSION 1 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; (LOCK on ROW with id ‘ALICE’) UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; // SESSION 2 BEGIN; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; (LOCK on ROW with id ‘BOB’) Sumedh Pathak | Citus Data | QCon London 2018
  52. Example // SESSION 1 BEGIN; UPDATE accounts SET balance =

    balance - 100 WHERE id = ‘ALICE’; (LOCK on ROW with id ‘ALICE’) UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; // SESSION 2 BEGIN; UPDATE accounts SET balance = balance + 100 WHERE id = ‘BOB’; (LOCK on ROW with id ‘BOB’) UPDATE accounts SET balance = balance - 100 WHERE id = ‘ALICE’ Sumedh Pathak | Citus Data | QCon London 2018
  53. How do Relational DB’s solve this? S1 S2 - Construct

    a Directed Graph - Each node is a session/transaction - Edge represents a wait on a lock Waiting on ‘Bob’ Waiting on ‘Alice’
  54. S1 S2 S1 S2 S2 S1 S2 Waits on S1

    S1 Waits on S2 Sumedh Pathak | Citus Data | QCon London 2018
  55. S1 S2 S1 S2 S2 S1 S2 Waits on S1

    S1 Waits on S2 Distributed Deadlock Detector S1 S2 S2 S1 Sumedh Pathak | Citus Data | QCon London 2018
  56. keys to scaling transactions - 2PC to ensure atomic transactions

    across nodes - Deadlock Detection—to scale complex & concurrent transaction workloads - MVCC - Failure Handling 4
  57. It’s 2018. Distributed can be Relational - Scale tables—via sharding

    - Scale SQL—via distributed relational algebra - Scale transactions—via 2PC & Deadlock Detection Sumedh Pathak | Citus Data | QCon London 2018 Now, how do we implement all of this?
  58. Postgres Sumedh Pathak | Citus Data | QCon London 2018

  59. PostgreSQL Planner Executor Custom scan Commit / abort Extension (.so)

    Access methods Foreign tables Functions ... ... ... ... ... ... ... CREATE EXTENSION ...
  60. PostgreSQL PostgreSQL PostgreSQL shards shards shard shards shards shard SELECT

    … FROM distributed_table … SELECT … FROM shard… SELECT … FROM shard… Citus
  61. PostgreSQL Citus PostGIS PL/Python JSONB 2PC Replication ... Sequences Indexes

    Full-Text Search Transactions … dblink Sumedh Pathak | Citus Data | QCon London 2018 Rich ecosystem of tooling, data types, & extensions in PostgreSQL
  62. PostgreSQL can be extended into an all-purpose distributed RDBMS

  63. I believe the future of distributed databases is relational.

  64. Thank you! Sumedh Pathak sumedh@citusdata.com @moss_toss | @citusdata | citusdata.com

    QCon London 2018 | The Future of Distributed Databases is Relational