Ep11. Designing Data-Intensive Applications - Partitioning

Eng Cafe

这一期我们讨论Designing Data-Intensive Applications书中partitioning这一章的学习笔记。

🔴 这一期偏重技术话题，我们会用很多英文表述技术性专有名词。之前有朋友反馈过中英夹杂对大家收听不方便，希望在意的朋友见谅。如果有不准确或者过时的地方欢迎指正。

# Show Notes

• 📕 Designing Data-Intensive Applications
• What is partitioning?
  • A partition is a division of a logical database or its constituent elements into distinct independent parts.
• Main reason: scalability - the query load can be distributed across many processors.
• Youtube / Vitess scaling story
  • Single MySQL → Add read replica → Write can’t catchup up → Partition
• How to partition?
• Partitioning by Key Range (e.g., Bigtable)
  • Assign a continuous range of keys to each partition
  • Pro: range scan is easier, data locality
  • Cons: certain access patterns can lead to hot spots (timestamp)
  • Cons: finding split points and managing rebalancing is hard
• Partitioning by Hash
  • Good hash function: uniformly distribute keys
  • Con: no easy range queries
• Cassandra does KKV (partitioning key, sort key, value)
• Hot spots: 3% of Twitter's Servers Dedicated to Justin Bieber
• Secondary indexes: Local index
  • Efficient write, expensive read
  • ElasticSearch
• Secondary indexes: Global index
  • Efficient read, expensive write
  • Using Global Secondary Indexes in DynamoDB (这里说错了，DynamoDB 支持 20 global secondary indexes per table）
• Rebalancing partitions
  • Move loads to other nodes
• Fixed number of partitions
  • New node steals partitions from every existing node
• Notion: 480 partitions
• Dynamic partitioning
  • 📈: split partition into 2
  • 📉: merge 2 partitions into 1
• Fixed number of partitions per node
  • https://www.datastax.com/blog/new-token-allocation-algorithm-cassandra-30
• Operations: full automatic (dangerous) / semi-automatic / full manual (tedious)
• Request Routing
  • 3 approaches: nodes talk to each other, separate routing tier, smart client
  • Separate coordination service such as ZooKeeper
• Notes by xg

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