1. Spanner: Becoming a SQL System
Xiyang Feng
CS848

2. Outline Introduction Spanner Recap Query Distribution
Query Range Extraction
Query Restarts
Comments
Summary

3. Introduction Globally distributed database Worldwide replication
OLTP DBMS for structured data
Tens of millions of QPS, hundreds of petabytes of data
Features:
Distributed query processor
Standard optimization: parallel processing, partition pruning
Distributed Query execution; Range extraction; Query restarts

4. Spanner Recap Database is horizontally row-range sharded
Shards are distributed across servers within a datacenter
Shards are replicated to separated datacenters
Parent – child table relation
Child table co-located with parent table
Interleaved data layout

5. Spanner Recap – Relation Data Layout
SingerId
SingerName 1
Beatles 2 U2 3
Pink Floyd
SingerId
AlbumId
AlbumName 1
Help! 2
Abbey Road 3
The Wall

6. Spanner Recap – Interleaved Data Layout1
Beatles
Help! 2
Abbey Road U2 3
Pink Floyd
The Wall

7. Query Distribution – DistributedUnion
Operator tree -> optimization by equivalent rewrite
A fundamental distribution operator
Scan(T) -> DistributedUnion[shard ⊆ T](Scan(shard))
Push down operation to shard
Op = OpFinal ◦ OpLocal, is an algebraic aggregation
Op(DistributedUnion[shard ⊆ T](Scan(shard))) = OpFinal(DistributedUnion[shard ⊆ T](OpLocal(Scan(shard))))

8. Query Distribution - Example

9. Query Distribution Distributed execution
Extract a set of shard, typically minimal
Detect local shard
Distributed join
Naïve approach – 1 cross-machine call / row
Batch manner
Batches of rows from input to remote subquery
Rows from each batch to original join’s subquery locally on a shard

10. Query Range Extraction
What portion of tables are referenced by the query
Intervals of primary key values
Range extraction:
Distribution range extraction
Seek range extraction
Lock range extraction

11. Query Range Extraction - Rewrite
Rewrite to a tree of correlated self-joins
SELECT d.*
FROM Document d
WHERE d.ProjectId
AND START_WITH(d.DocumentPath, ‘/proposal’)
AND d.Version 2

12. Query Restarts Compensate for failure, resharding … Benefits
Hiding transient failure
No retry loop
Streaming pagination through query results
Forward progress for long-running queries
Recurrent rolling upgrades
Simpler Spanner internal error handling

13. Query Restarts - Challenges
Dynamic resharding
Non-determinism
Restarts across server version
Restart token wire format
Query plan
Operator behaviour

14. Comments/Questions Inadequate experiment
“The cost of range extraction may however outweigh the benefit of converting full scan to seeks …”
Hotspotting and load balancing
CA or CP?

15. Summary Globally distributed database with SQL functionality
Standard implementation with unique capabilities
Query Distribution
Range Extraction
Query Restart
From NoSQL to SQL