1. Netflix running Presto in the AWS Cloud
Zhenxiao Luo
Senior Software Engineer @ Netflix

2. Outline
● BigDataPlatform@Netflix
● Use cases & requirements
● What we did
○ Reading/Writing from/to Amazon S3
○ Operations
○ Deployment
○ Performance
● What’s next?

3. BigDataPlatform @ Netflix

4. Use Cases
● Big Batch Jobs
○ high throughput, fault tolerant, ETL
○ data spills to disk
○ Hive on Tez, Pig on Tez
● Adhoc Queries
○ low latency, interactive, data exploration
○ in-memory, but limited data size
○ Impala, Redshift, Spark, Presto

5. Netflix Requirement
● SQL like Language
● Low latency for adhoc queries
● Work well on AWS cloud
● Good integration with Hadoop stack
● Scale to 1000+ node cluster
● Open source with community support

6. What did Netflix do?

7. Reading/Writing to/from S3
● Option 1: Apache Hadoop NativeS3FileSysyem
● Option 2: PrestoS3FileSystem
○ retry logic for read timeout
○ write directly to final S3 path
● Option 3: emrFileSystem
○ disable hadoop logging
○ disable hadoop FileSystem cache

8. Bug Fixes
● https://github.
com/facebook/presto/commit/cf0b2d66f4050fb1959c832809fa76e323d6d4
6e
● https://github.
com/facebook/presto/commit/594b06c3e93a482dc162d2c49c9bd265795ef
b86
● https://github.com/facebook/presto/pull/1147
● https://github.com/facebook/presto/pull/1300
● https://github.com/facebook/presto/issues/1285
● https://github.com/facebook/presto/issues/1264

9. Our Operations Environment
● Launch script on top of EMR
● Ganglia integration
● Usage graphs - concurrent queries & tasks

10. Current Deployment
● Presto in Production @ Netflix
● 100+ nodes Presto Cluster
● 1000+ queries running per day
● Presto query against the same Petabyte Scale S3 Data
Warehouse as Hive and Pig

11. Observed Performance @ Netflix
● Data in Sequence File Format
● One MapReduce Job SmallTableScan
○ MapReduce overhead dominates the query execution time
○ Presto is always ~10X faster than Hive
● One MapReduce Job BigTableScan
○ MapReduce overhead is marginal compared with big table scan time
○ Presto performs similar to Hive
● Multiple MapReduce Aggregation
○ Presto is always > 10X faster than Hive
● Joins
○ Presto is always > 2X faster than Hive

12. What we are working on
● Support Parquet File Format
○ https://github.com/facebook/presto/pull/1147
○ Parquet performs similar to Sequence, but not as fast as RCFile
● ODBC/JDBC driver for Presto
○ Support Microstrategy running on Presto

13. Some inconveniences ...
● Support Server Side “Use Schema”
○ Workaround: Client Side “Use Schema” Or “Schema.Table”
● Recurse the partition directory
○ Different behavior with Hive
● Metadata caching
○ have to rerun the query a number of times to see the metadata
change
● Extend JSON extract functions to allow . notation
○ json_extract_scalar(mapColumn, '$.namePart1.namePart2')
○ Workaround: regexp_extract
● WebUI running slow
○ load query task info on demand

14. Features we would like
● Big table join
● User Defined Functions
● Break down one column value into several tuples
○ In Hive: lateral view explode json_tuple
● Decimal type
● Scheduler
● Writes
○ Insert overwrite
○ Alter table add partition
○ Parallel writes from workers (not client only)

15. Q & A
Thank you!