This document summarizes Presto, an analytics engine used at Facebook. It provides ad-hoc querying for data warehouses and batch processing. It is used for analytics across Facebook's data warehouses and specialized data stores. The document outlines Presto's architecture, deployment, usage statistics, features, and enhancements made for specific Facebook use cases including user-facing products, large datasets, and reliable data loading.

1. Presto @ Facebook
Martin Traverso and Dain Sundstrom

2. Presto @ Facebook
• Ad-hoc/interactive queries for warehouse
• Batch processing for warehouse
• Analytics for user-facing products
• Analytics over various specialized stores

3. Analytics for Warehouse

4. Architecture
UI CLI Dashboards Other tools
Gateway
Presto Presto
Warehouse
Cluster
Warehouse
Cluster

5. Deployment
Presto
HDFS
Datanode
MR
HDFS
Datanode
MR
HDFS
Datanode
Presto
HDFS
Datanode
MR
HDFS
Datanode

6. Stats
• 1000s of internal daily active users
• Millions of queries each month
• Scan PBs of data every day
• Process trillions of rows every day
• 10s of concurrent queries

7. Features
• Pipelined partition/split enumeration
• Streaming
• Admission control
• Resource management
• System reliability

8. Batch workloads

9. Batch Requirements
• INSERT OVERWRITE
• More data types
• UDFs
• Physical properties (partitioning, etc)

10. Analytics for
User-facing Products

11. Requirements
• Hundreds of ms to seconds latency, low variability
• Availability
• Update semantics
• 10 - 15 way joins

12. Architecture
Loader
Presto
Worker
Presto
Worker
Presto
Worker
MySQL
Loader
MySQL
MySQL
Client

13. Stats
• > 99.99% query success rate
• 100% system availability
• 25 - 200 concurrent queries
• 1 - 20 queries per second
• <100ms - 5s latency

14. Presto Raptor

15. Requirements
• Large data sets
• Seconds to minutes latency
• Predictable performance
• 5-15 minute load latency
• Reliable data loads (no duplicates, no missing data)
• 10s of concurrent queries

16. Basic Architecture
Coordinator
MySQL
Worker Flash
Worker Flash
Worker Flash
Client

17. But isn’t that exactly what
Hive does?

18. Additional Features
• Full featured and atomic DDL
• Table statistics
• Tiered storage
• Atomic data loads
• Physical organization

19. Table Statistics
• Table is divided into shards
• Each shard is stored in a separate replication unit (i.e., file)
• Typically 1 to 10 million rows
• Node assignment and stats stored in MySQL

20. Table Schema in MySQL
Tables
id name
1 orders
2 line_items
3 parts
table1 shards
uuid nodes c1_min c1_max c2_min c2_max c3_min c3_max
43a5 A 30 90 cat dog 2014 2014
6701 C 34 45 apple banana 2005 2015
9c0f A,D 25 26 cheese cracker 1982 1994
df31 B 23 71 tiger zebra 1999 2006

21. Tiered Storage
Coordinator
MySQL
Worker Flash
Worker Flash
Worker Flash
Client Backup

22. Tiered Storage
• One copy in local, expensive, flash
• Backup copy in cheap durable backup tier
• Currently Gluster internally, but can be anything durable
• Only assumes GET and PUT with client assigned ID methods

23. Atomic Data Loads
• Import data periodically from streaming event system
• Internally a Scribe based system similar to Kafka or Kinesis
• Provides continuation tokens
• Loads performed using SQL

24. Atomic Data Loads
INSERT INTO target
SELECT *
FROM source_stream
WHERE token BETWEEN ${last_token} AND ${next_token}

25. Loader Process
1. Record new job with “now” token in MySQL
2. Execute INSERT from last committed token to “now” token with
external batch id
3. Wait for INSERT to commit (check external batch status)
4. Record job complete
5. Repeat

26. Failure Recovery
• Loader crash
• Check status of jobs using external batch id
• INSERT hang
• Cancel query and rollback job (verify status to avoid race)
• Duplicate loader processes
• Process guarantees only one job can complete
• Monitor for lack of progress (catches no loaders also)

27. Physical Organization
• Temporal organization
• Assure files don’t cross temporal boundaries
• Common filter clause
• Eases retention policies
• Sorted files
• Can reduce file sections processed (local stats)
• Can reduce shards processed

28. Unorganized Data
Sort Columns
Time

29. Organized Data
Sort Columns
Time

30. Background Organization
• Compaction
• Balance data
• Eager data recover (from backup)
• Garbage collection
• Junk created by compaction, delete, balance, recovery

31. Future Use Cases
• Hot data cache for Hadoop data
• 0-N local copies of “backup” tier
• Query results cache
• Raw, not rolled-up, data store for Sharded MySql customers
• Materialized view store