Challenges of Distributing Postgres: A Citus Story | QCon San Francisco 2017 | Ozgun Erdogan

Set theory forms the basis for relational algebra and relational databases, and SQL is the lingua franca of modern RDBMS’s. Even with all the attention given to NoSQL in recent years, the lion share of database usage remains relational. But until recently, nearly all relational database solutions have been limited to the resources of a single node. Not anymore. This talk is about my team’s journey tackling the challenges of distributing SQL. Specifically in the context of my favorite (open source) database: Postgres. I believe that too many developers spend too much time worrying about scaling their databases. So at Citus Data, we created an extension to Postgres that enables developers to scale out compute, memory, and storage by distributing queries across a cluster of nodes. This talk describes the distributed systems challenges we faced at Citus in scaling out Postgres—and how we addressed them. I’ll talk about how we use PostgreSQL’s extension APIs to parallelize queries in a distributed cluster. I’ll cover the architecture of a distributed query planner and specifically how the join order planner has to choose between broadcast, co-located, and repartition joins in order to minimize network I/O. And if there’s time, I’ll walk through the dynamic executor logic that we built. The end result: a distributed database and a lot less time spent worrying about scale.

Transcript

1. Challenges of Distributing
   Postgres:

   A Citus Story
   Ozgun Erdogan
   QCon San Francisco | November 2017
   

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2. Developers Love Postgres
   PostgreSQL
   MySQL
   MongoDB
   SQL Server +
   Oracle
   0%
   18%
   35%
   53%
   70%
   Mar-11 Sept-11 Mar-12 Sept-12 Mar-13 Sept-13 Mar-14 Sept-14 Mar-15 Sept-15 Mar-16
   RDBMS: PostgreSQL, MySQL, Microsoft SQL Server, Oracle
   Ozgun Erdogan | QCon San Francisco 2017
   

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3. I love Postgres, too
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   Ozgun Erdogan
   CTO of Citus Data
   Distributed Systems
   Distributed Databases
   Formerly of Amazon
   Love drinking margaritas
   

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5. Our mission at Citus Data
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   Make it so SaaS businesses

   never have to worry about
   scaling their database again
   

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6. What is the Citus database?
   1.Scales out PostgreSQL
   2.Extension to PostgreSQL
   3.Available in 3 Ways
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   • Using sharding & replication
   • Query engine parallelizes SQL queries across many nodes
   • Using PostgreSQL extension APIs
   

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7. Citus, Packaged Three Ways
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   Open

   Source
   Enterprise

   Software
   Fully-Managed

   Database as a Service
   github.com/citusdata/citus
   

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8. Simplified Citus Architecture
   

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9. 3 Challenges Distributing Postgres
   1. PostgreSQL and High Availability
   2. PostgreSQL is huge. How to keep up with it
   3. Distributed transactions
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10. PostgreSQL &

    High Availability (HA)
    Designing for a Cloud-native world
    1
    

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11. Why is High Availability hard?
    PostgreSQL replication uses one primary &
    multiple secondary nodes. Two challenges:
    1. Most Postgres clients aren’t smart. When the
    primary fails, they retry the same IP.
    2. Postgres replicates entire state. This makes it
    resource intensive to reconstruct new nodes from a
    primary.
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12. Database Failures Should Be Transparent
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13. Database Failures Shouldn’t Be a Big Deal
    1. PostgreSQL streaming replication to replicate from
    primary to secondary. Back up to S3.
    2. Volume level replication to replicate to secondary’s
    volume. Back up to S3.
    3. Incremental backups to S3. Reconstruct secondary
    nodes from S3.
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    3 Methods for HA & Backups in Postgres
    

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14. Postgres - Streaming Replication (1)
    Write-ahead logs
    (streaming repl.)
    Table foo
    Primary –
    PostgreSQL
    streaming repl.
    Table bar
    WAL logs
    Table foo
    Table bar
    WAL logs
    Secondary –
    PostgreSQL
    streaming repl.
    Monitoring Agents -
    streaming repl.
    setup & auto failover
    S3 / Blob Storage
    (Encrypted)
    Backup
    Process
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15. Postgres – AWS RDS & Azure (2)
    Postgres
    Primary
    Monitoring Agents
    (Auto node failover)
    Persistent Volume
    Postgres
    Standby
    S3 / Blob Storage
    (Encrypted)
    Table foo
    Table bar
    WAL logs
    Table foo
    Table bar
    WAL logs
    Backup process
    Backup
    Process
    Persistent Volume
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16. Postgres – Reconstruct from WAL (3)
    Postgres
    Primary
    Monitoring Agents
    (Auto node failover)
    Persistent Volume
    Postgres
    Secondary
    Backup
    Process
    S3 / Blob Storage
    (Encrypted)
    Table foo
    Table bar
    WAL logs
    Persistent Volume
    Table foo
    Table bar
    WAL logs
    Backup process
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17. WHO DOES THIS? PRIMARY BENEFITS
    Streaming Replication
    (local / ephemeral disk)
    On-prem

    Manual EC2
    Simple to set up
    Direct I/O: High I/O & large storage
    Disk Mirroring
    RDS

    Azure Preview
    Works for MySQL and PostgreSQL

    Data durability in cloud environments
    Reconstruct from WAL
    Heroku

    Citus Cloud
    Enables Fork and PITR
    Node reconstruction in background
    (Data durability in cloud environments)
    How do these approaches compare?
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18. Summary
    • In PostgreSQL, a database node’s state gets
    replicated in its entirety. The replication can be set up
    in three ways.
    • Reconstructing a secondary node from S3 makes
    bringing up or shooting down nodes easy.
    • When you shard your database, the state you need to
    replicate per node becomes smaller.
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19. PostgreSQL is huge
    How do you scale all features?
    2
    

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20. 3 ways to build a distributed database
    1. Build a distributed database from scratch
    2. Middleware sharding (mimic the parser)
    3. Fork your favorite database (like PostgreSQL)
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21. Example Transaction Block
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22. Postgres Features, Tools & Frameworks
    • PostgreSQL manual (US Letter)
    • Clients for diff programming
    languages
    • ORMs, libraries, GUIs
    • Tools (dump, restore, analyze)
    • New features
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23. At First, Forked PostgreSQL with Style
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24. Two Stage Query Optimization
    1. Plan to minimize network I/O
    2. Nodes talk to each other using SQL over libpq
    3. Learned to cooperate with planner / executor bit by bit
    (Volcano style executor)
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25. Citus Architecture (Simplified)
    25
    SELECT avg(revenue)
    FROM sales
    Coordinator
    SELECT sum(revenue), count(revenue)
    FROM table_1001
    SELECT sum … FROM table_1003
    Worker node 1
    Table metadata
    Table_1001
    Table_1003
    SELECT sum … FROM table_1002
    SELECT sum … FROM table_1004
    Worker node 2
    Table_1002
    Table_1004
    Worker node N
    .
    .
    .
    .
    .
    .
    Each node PostgreSQL with Citus installed
    1 shard = 1 PostgreSQL table
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26. Unfork Citus using Extension APIs
    CREATE EXTENSION citus;
    • System catalogs – Distributed metadata
    • Planner hook – Insert, Update, Delete, Select
    • Executor hook – Insert, Update, Delete, Select
    • Utility hook – Alter Table, Create Index, Vacuum, etc.
    • Transaction & resources handling – file descriptors, etc.
    • Background worker process – Maintenance processes
    (distributed deadlock detection, task tracker, etc.)
    • Logical decoding – Online data migrations
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27. PostgreSQL has transactions.
    How to handle distributed transactions
    3
    

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28. BEGIN
    INSERT
    UPDATE
    SELECT
    COMMIT
    ROLLBA
    CK
    

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29. Consistency in Distributed Databases
    1. 2PC: All participating nodes need to be up
    2. Paxos: Achieves consensus with quorum
    3. Raft: More understandable alternative to
    Paxos
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30. Concurrency in Distributed Databases
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31. Locks
    

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32. What is a Lock?
    • Protects against concurrent modifications.
    • Locks are released at the end of a
    transaction.
    

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33. Transactions Block on 1st Conflicting Lock
    

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34. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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35. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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36. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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37. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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38. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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39. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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40. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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41. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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42. Transactions and Concurrency
    • Transactions that don’t modify the same
    row can run concurrently.
    

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43. Distributed transactions are a complex topic
    • Most articles on distributed transactions focus on data
    consistency.
    • Data consistency is only one side of the coin. If you’re
    using a relational database, your application benefits
    from another key feature: deadlock detection.
    • https://www.citusdata.com/blog/2017/08/31/
    databases-and-distributed-deadlocks-a-faq
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44. So now what? We talked about 3
    challenges distributing Postgres
    1. PostgreSQL, Replication, High Availability
    2. Tradeoffs in building a distributed database—
    and how we chose PostgreSQL’s extension
    APIs
    3. Distributed deadlock detection & distributed
    transactions
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45. 45
    Solving a very complex problem
    

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46. 46
    “SQL is hard, not impossible, to scale”
    

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47. © 2017 Citus Data. All right reserved.
    ozgun@citusdata.com
    Questions?
    @citusdata
    Ozgun Erdogan
    www.citusdata.com
    

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