Presto @ FacebookMartin Traverso and Dain Sundstrom
Presto @ Facebook
• Ad-hoc/interactive queries for warehouse
• Batch processing for warehouse
• Analytics for user-facing products
• Analytics over various specialized stores
Analytics for Warehouse
ArchitectureUI CLI Dashboards Other tools
Gateway
Presto Presto
WarehouseCluster
WarehouseCluster
Deployment
Presto
HDFS Datanode
MR
HDFS Datanode
MR
HDFS Datanode
Presto
HDFS Datanode
MR
HDFS Datanode
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
Features• Pipelined partition/split enumeration
• Streaming
• Admission control
• Resource management
• System reliability
Batch workloads
Batch Requirements
• INSERT OVERWRITE
• More data types
• UDFs
• Physical properties (partitioning, etc)
Analytics for User-facing Products
Requirements
• Hundreds of ms to seconds latency, low variability
• Availability
• Update semantics
• 10 - 15 way joins
Architecture
Loader
PrestoWorker
PrestoWorker
PrestoWorker
MySQL
Loader
MySQL
MySQL
Client
Stats• > 99.99% query success rate
• 100% system availability
• 25 - 200 concurrent queries
• 1 - 20 queries per second
• <100ms - 5s latency
Presto Raptor
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
Basic Architecture
Coordinator
MySQL Worker Flash
Worker Flash
Worker Flash
Client
But isn’t that exactly what Hive does?
Additional Features• Full featured and atomic DDL
• Table statistics
• Tiered storage
• Atomic data loads
• Physical organization
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
Table Schema in MySQL
Tablesid name1 orders2 line_items3 parts
table1 shardsuuid nodes c1_min c1_max c2_min c2_max c3_min c3_max43a5 A 30 90 cat dog 2014 20146701 C 34 45 apple banana 2005 20159c0f A,D 25 26 cheese cracker 1982 1994df31 B 23 71 tiger zebra 1999 2006
Tiered Storage
Coordinator
MySQL Worker Flash
Worker Flash
Worker Flash
Client Backup
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
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
Atomic Data Loads
INSERT INTO target SELECT * FROM source_stream WHERE token BETWEEN ${last_token} AND ${next_token}
Loader Process1. 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
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)
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
Unorganized Data
Sort Columns
Tim
e
Organized Data
Sort Columns
Tim
e
Background Organization• Compaction
• Balance data
• Eager data recover (from backup)
• Garbage collection
• Junk created by compaction, delete, balance, recovery
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