Big dataproposal
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This document discusses Hive and Presto for big data analytics in the cloud. It provides an overview of how big data has evolved from traditional analytics on internal data to using new external data sources at larger scales. It describes how the public cloud has changed the economics and flexibility of big data projects by providing cheap storage, elastic compute, and open-source big data software like Hadoop, Hive, and Presto. It compares Hive, which uses Hadoop MapReduce for execution, to Presto, which uses an in-memory pipelined execution model, and shows how Presto can provide faster performance for interactive queries.
Big dataproposal
- 1. HIVE AND PRESTO FOR BIG DATA ANALYTICS IN THE CLOUD
- 2. WHAT’S NEW ABOUT BIG DATA YOU SAY… Traditionally, analytics on data internal to an organization Customer data ERP data Some pre-digested external data like market research Sophisticated analytics using new data sources Social data Website data Low density, fine grained and massive “Most EDWs are < 2TB”
- 3. LOW DENSITY, HIGH VOLUME DATA Amul comment data: 18000 * 140 * 60 * 24 * 30 = 100 GB per month Category Unique visitors Retail – Luxury goods 20 million Retail – consumer goods 30 million Retail – tickets 26 million Social Media Website data Traditional technologies cannot handle this low-density, high volume data
- 4. SKELETON OF A BIG DATA PROJECT Internal Data External Data TB - PBs Actionable report Analytics Workflow
- 5. HOW DO THE BIG GUYS DO IT? Build data centers Buy or build custom big-data software Hire ETL engineers who manage bringing data into the system Hire admins to keep it all running Hire data scientists to come up with interesting questions Hire developers who can translate questions into programs
- 6. Lots of upfront investment Long time to get started Lots of risks BIG DATA PROJECT ENTAILS
- 7. LANDSCAPE IS CHANGING Advent of public clouds Cheap, reliable storage Provision 10-1000s of machines in a couple of minutes Pay as you go, grow as you please Free / inexpensive big-data software Hadoop, Hive, Presto
- 8. CLOUD PRIMITIVES Persistent object store e.g. AWS S3 Reliability is basically solved for you (*) Ability to provision clusters with pre-built images in a couple of minutes Pay by the hour (or by the minute) Spot instances (AWS) Relational DB as a Service MySQL, PostgreSQL etc
- 9. THE CLOUD CAN HANDLE YOUR DATA
- 10. CLOUD’S COMPUTE FLEXIBILITY Analytics workloads tend to be bursty Most orgs struggle to predict usage 2-3 months down the line Tend to overprovision compute Result: < 30% utilization of their hardware Cloud allows you to scale up and down Trickier for a big data system, but possible Chen et al, VLDB 2012 Provision for peak workload
- 11. BIG DATA SOFTWARE Many open source projects Hadoop based on Google’s MR paper (Yahoo) Hive (SQL-on-Hadoop) Presto (Fast SQL) Production ready, running at scale at Yahoo, FB and many other environments
- 12. ENTER HADOOP Open-source implementation of Map-reduce used by Google to index trillions of web pages Allows programmers to write distributed programs using map and reduce abstractions Ability to run these programs on large amounts of data Uses bunch of cheap hardware, can tolerate failures
- 13. HADOOP SCALES!
- 14. HIVE: SQL ON HADOOP Facebook had a Multi Petabyte Warehouse Had 80+ engineers writing Hadoop jobs Files are insufficient data abstractions Need tables, schemas, partitions, indices SQL is highly popular So, implement SQL on top of Hadoop Allowed non-programmers to process all the data FB open-sourced it Production ready Processes 25PB of data in FB Processes 20PB of data at Qubole
- 15. HIVE ALLOWS YOU TO DESCRIBE DATA Example My data lives in Amazon S3 in a specific location It is in delimited text format Please create a virtual table for me Number of data formats: JSON, Text, Binary, Avro, ProtoBuf, Thrift Analytics is often a downstream process Conversion of data is time consuming and not productive create external table nation (N_NATIONKEY INT, N_NAME STRING, N_REGIONKEY INT, N_COMMENT STRING) ROW FORMAT DELIMITED STORED AS TEXTFILE LOCATION 's3n://public-qubole/datasets/tpch5G/nation';
- 16. HIVE EXTENSIBILITY Connect to external data sources like MongoDB Write code to understand new data formats - serdes Custom UDFs in Java Plug in custom code in python or any other language SELECT TRANSFORM (hosting_ids, user_id, d) USING 'python combine_arrays.py' AS (hosting_ranks_array, user_id, d) FROM s_table;
- 17. HIVE ALLOWS YOU TO QUERY THE DATA SQL-Like Query is parallelized using Hadoop as execution engine Select count(*) from nation; Count(*) Count(*) Sum()
- 18. HIVE EXECUTION Split Hive query into multiple Hadoop/MR jobs Run Job 1, save intermediate output to HDFS Run Job 2.. Return results Data parallel because every hadoop job runs on number of machines T11 100MB T12 100MB 10 files 5 files 5 files
- 19. TASK PARALLELISM T1 T2 T3 100MB 100MB 100MB10 files
- 20. EXECUTION MODEL 1 T1 100MB T2 100MB T3 100MB10 files • Only 100MB of memory required • Can stop and resume • Allows for multiplexing multiple pipelines • Can tolerate failures • Spilling can be expensive • Time to first result is high
- 21. EXECUTION MODEL 2 T1 100MB T2 100MB T3 100MB10 files • Task parallelism • Needs 3X memory • No spilling, hence much faster • Early first results • Stop and resume is trickier • Multiplexing is more difficult • Cannot tolerate failures
- 22. ENTER PRESTO Hive was EM1 and had associated disadvantages Internal project at Facebook to implement EM2 (Presto) Use case was interactive queries over the same data Open sourced late 2013 Promised much faster query performance In-memory processing, aggressive pipelining Supports all the data formats that Hive does Can’t plug in user code at this point, vanilla SQL
- 23. CONTRASTING HIVE AND PRESTO Hive Presto Uses Hadoop MR for execution (EM1) Pipelined execution model (EM2) Spills intermediate data to FS Intermediate data in memory Can tolerate failures Does not tolerate failures Automatic join ordering User-specified join ordering Can handle joins of two large tables One table needs to fit in memory Supports grouping sets Does not support GS Plug in custom code Cannot plug in custom code More data types Limited data types Hive 0.11 vs Presto 0.60
- 24. PERFORMANCE COMPARISON • Presto is 2.5-7x faster • But, some queries just run out of memory • Contrasts the execution models
- 25. IN A NUTSHELL
- 26. SAMPLE SETUP Cloud Storage Sqoop Application Sync Heavy duty queries Interactive queries
- 27. CRYSTAL BALL Hive is actively working on task parallelism as part of the Stinger Initiative Presto is also making rapid progress in bridging some of its gaps There are other open source projects: Impala, Shark, Drill, Tajo Lots of goodies for users
- 28. CONCLUSION Big Data Analytics is becoming accessible and affordable Public clouds give flexibility and change economics Hive and Presto provide intuitive and powerful ways to interact with your data