F1: The Fault-Tolerant Distributed RDBMS Supporting Google's Ad Business

Table of Contents

http://research.google.com/pubs/pub38125.html @ 2012

1 Today's Talk

  • F1 - A Hybrid Database combining the
    • Scalability of Bigtable
    • Usability and functionality of SQL databases
  • Key Ideas
    • Scalability: Auto-sharded storage
    • Availability & Consistency: Synchronous
      • High commit latency: Can be hidden
        • Hierarchical schema (spanner提供层级schema)
        • Protocol buffer column types
        • Efficient client code
      • Can you have a scalable database without going NoSQL? Yes.

2 Our Legacy DB: Sharded MySQL

  • Sharding Strategy
    • Sharded by customer
    • Apps optimized using shard awareness
  • Limitations
    • Availability
      • Master / slave replication -> downtime during failover
      • Schema changes -> downtime for table locking
    • Scaling
      • Grow by adding shards
      • Rebalancing shards is extremely difficult and risky
      • Therefore, limit size and growth of data stored in database
    • Functionality
      • Can't do cross-shard transactions or joins

3 Our Solution: F1

  • A new database
    • built from scratch,
    • designed to operate at Google scale,
    • without compromising on RDBMS features.
    • Co-developed with new lower-level storage system, Spanner
  • Underlying Storage - Spanner
    • Descendant of Bigtable, Successor to Megastore
    • Properties
      • Globally distributed
      • Synchronous cross-datacenter replication (with Paxos)
      • Transparent sharding, data movement
      • General transactions
        • Multiple reads followed by a single atomic write
        • Local or cross-machine (using 2PC)
      • Snapshot reads
    • Hierarchical Schema
      • Explicit table hierarchies. Example:
      • Customer (root table): PK (CustomerId)
      • Campaign (child): PK (CustomerId, CampaignId)
      • AdGroup (child): PK (CustomerId, CampaignId, AdGroupId)
    • Clustered Storage
      • Child rows under one root row form a cluster (属于同一个root的数据形成一个cluster)
      • Cluster stored on one machine (unless huge) (然后这个cluster通常会存放在一个机器上面)
      • Transactions within one cluster are most efficient
      • Very efficient joins inside clusters (can merge with no sorting)
    • Protocol Buffer Column Types (这个应该有很多好处,能够在很大程度上减少表的数量以及减少阻抗)

f1-hierarchical-schema.png

f1-clustered-storage.png

  • Architecture
    • Sharded Spanner servers
      • data on GFS and in memory
    • Stateless F1 server
    • Pool of workers for query execution

f1-architecture.png

  • Features
    • Relational schema
      • Extensions for hierarchy and rich data types
      • Non-blocking schema changes
    • Consistent indexes
    • Parallel reads with SQL or Map-Reduce

4 How We Deploy

  • Five replicas needed for high availability
    • Why not three?
      • Assume one datacenter down
      • Then one more machine crash => partial outage
    • Replicas spread across the country to survive regional disasters
      • Up to 100ms apart(传输距离达到了100ms以上?)
  • Performance
    • Very high commit latency - 50-100ms(因为写需要跨越几个机房)
    • Reads take 5-10ms - much slower than MySQL(如果单次读取在5-10ms算是比较快的了)
    • High throughput
  • Coping with High Latency
    • Preferred transaction structure
      • One read phase: No serial reads
      • Read in batches
    • Read asynchronously in parallel
      • Buffer writes in client, send as one RPC
    • Use coarse schema and hierarchy(通过提供protobuf column type来提供粗粒度的schema)
      • Fewer tables and columns
      • Fewer joins
    • For bulk operations
      • Use small transactions in parallel - high throughput
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