Cloudera Impala presentation
- 1. Cloudera Impala: A Modern SQL Engine for Apache Hadoop Mark Grover Software Engineer, Cloudera February 27, 2013
- 2. What is Impala? ● General-purpose SQL engine ● Real-time queries in Apache Hadoop ● Beta version released since October 2012 ● General availability (GA) release slated for April 2013 ● Open source under Apache license
- 3. Overview ● User View of Impala ● Architecture of Impala ● Comparing Impala with Dremel ● Comparing Impala with Hive ● Impala Roadmap
- 4. Impala Overview: Goals ● General-purpose SQL query engine: ● should work both for analytical and transactional workloads ● will support queries that take from milliseconds to hours ● Runs directly within Hadoop: ● reads widely used Hadoop file formats ● talks to widely used Hadoop storage managers ● runs on same nodes that run Hadoop processes ● High performance: ● C++ instead of Java ● runtime code generation ● completely new execution engine that doesn't build on MapReduce
- 5. User View of Impala: Overview ● Runs as a distributed service in cluster: one Impala daemon on each node with data ● User submits query via ODBC/JDBC to any of the daemons ● Query is distributed to all nodes with relevant data ● If any node fails, the query fails ● Impala uses Hive's metadata interface, connects to Hive's metastore ● Supported file formats: ● uncompressed/lzo-compressed text files ● sequence files and RCFile with snappy/gzip compression ● GA: Avro data files ● GA: columnar format (more on that later)
- 6. User View of Impala: SQL ● SQL support: ● patterned after Hive's version of SQL ● essentially SQL-92, minus correlated subqueries ● limited to Select, Project, Join, Union, Subqueries, Aggregation and Insert ● only equi-joins; no non-equi joins, no cross products ● Order By only with Limit ● GA: DDL support (CREATE, ALTER) ● Functional limitations: ● no custom UDFs, file formats, SerDes ● no beyond SQL (buckets, samples, transforms, arrays, structs, maps, xpath, json) ● only hash joins; joined table has to fit in memory: ● beta: of single node ● GA: aggregate memory of all (executing) nodes
- 7. User View of Impala: Apache HBase ● HBase functionality: ● uses Hive's mapping of HBase table into metastore table ● predicates on rowkey columns are mapped into start/stop row ● predicates on other columns are mapped into SingleColumnValueFilters ● HBase functional limitations: ● no nested-loop joins ● all data stored as text
- 8. Impala Architecture ● Two binaries: impalad and statestored ● Impala daemon (impalad) ● handles client requests and all internal requests related to query execution ● exports Thrift services for these two roles ● State store daemon (statestored) ● provides name service and metadata distribution ● also exports a Thrift service
- 9. Impala Architecture ● Query execution phases ● request arrives via odbc/jdbc ● planner turns request into collections of plan fragments ● coordinator initiates execution on remote impalad's ● during execution ● intermediate results are streamed between executors ● query results are streamed back to client ● subject to limitations imposed to blocking operators (top-n, aggregation)
- 10. Impala Architecture: Planner ● 2-phase planning process: ● single-node plan: left-deep tree of plan operators ● plan partitioning: partition single-node plan to maximize scan locality, minimize data movement ● Plan operators: Scan, HashJoin, HashAggregation, Union, TopN, Exchange ● Distributed aggregation: pre-aggregation in all nodes, merge aggregation in single node. GA: hash-partitioned aggregation: re-partition aggregation input on grouping columns in order to reduce per-node memory requirement ● Join order = FROM clause order GA target: rudimentary cost-based optimizer
- 11. Impala Architecture: Planner ● Example: query with join and aggregation SELECT state, SUM(revenue) FROM HdfsTbl h JOIN HbaseTbl b ON (...) GROUP BY 1 ORDER BY 2 desc LIMIT 10 TopN Agg TopN Agg Hash Hash Agg Join Join HDFS HBase Exch Exch Scan Scan HDFS HBase at coordinator at DataNodes at region servers Scan Scan
- 12. Impala Architecture: Query Execution Request arrives via odbc/jdbc SQL App Hive HDFS NN Statestore ODBC Metastore SQL request Query Planner Query Planner Query Planner Query Coordinator Query Coordinator Query Coordinator Query Executor Query Executor Query Executor HDFS DN HBase HDFS DN HBase HDFS DN HBase
- 13. Impala Architecture: Query Execution Planner turns request into collections of plan fragments Coordinator initiates execution on remote impalad's SQL App Hive HDFS NN Statestore ODBC Metastore Query Planner Query Planner Query Planner Query Coordinator Query Coordinator Query Coordinator Query Executor Query Executor Query Executor HDFS DN HBase HDFS DN HBase HDFS DN HBase
- 14. Impala Architecture: Query Execution Intermediate results are streamed between impalad's Query results are streamed back to client SQL App Hive HDFS NN Statestore ODBC Metastore query results Query Planner Query Planner Query Planner Query Coordinator Query Coordinator Query Coordinator Query Executor Query Executor Query Executor HDFS DN HBase HDFS DN HBase HDFS DN HBase
- 15. Impala Architecture ● Metadata handling: ● utilizes Hive's metastore ● caches metadata: no synchronous metastore API calls during query execution ● beta: impalad's read metadata from metastore at startup ● Post-GA: metadata distribution through statestore ● Post-GA: HCatalog
- 16. Impala Architecture ● Execution engine ● written in C++ ● runtime code generation for "big loops" ● internal in-memory tuple format plus fixed-width data at fixed offsets ● uses intrinsics/special cpu instructions for text parsing, crc32 computation, etc.
- 17. Impala Execution Engine ● More on runtime code generation ● example of "big loop": insert batch of rows into hash table ● known at query compile time: # of tuples in a batch, tuple layout, column types, etc. ● generate at compile time: unrolled loop that inlines all function calls, contains no dead code, minimizes branches ● code generated using llvm
- 18. Impala's Statestore ● Central system state repository ● name service (membership) ● Post-GA: metadata ● Post-GA: other scheduling-relevant or diagnostic state ● Soft-state ● all data can be reconstructed from the rest of the system ● cluster continues to function when statestore fails, but per-node state becomes increasingly stale ● Sends periodic heartbeats ● pushes new data ● checks for liveness
- 19. Statestore: Why not ZooKeeper? ● ZK is not a good pub-sub system ● Watch API is awkward and requires a lot of client logic ● multiple round-trips required to get data for changes to node's children ● push model is more natural for our use case ● Don't need all the guarantees ZK provides: ● serializability ● persistence ● prefer to avoid complexity where possible ● ZK is bad at the things we care about and good at the things we don't
- 20. Comparing Impala to Dremel ● What is Dremel? ● columnar storage for data with nested structures ● distributed scalable aggregation on top of that ● Columnar storage in Hadoop: joint project between Cloudera and Twitter ● new columnar format: Parquet; derived from Doug Cutting's Trevni ● stores data in appropriate native/binary types ● can also store nested structures similar to Dremel's ColumnIO ● Distributed aggregation: Impala ● Impala plus Parquet: a superset of the published version of Dremel (which didn't support joins)
- 21. More about Parquet ● What is it: ● container format for all popular serialization formats: Avro, Thrift, Protocol Buffers ● successor to Trevni ● jointly developed between Cloudera and Twitter ● open source; hosted on github ● Features ● rowgroup format: file contains multiple horiz. slices ● supports storing each column in separate file ● supports fully shredded nested data; repetition and definition levels similar to Dremel's ColumnIO ● column values stored in native types (bool, int<x>, float, double, byte array) ● support for index pages for fast lookup ● extensible value encodings
- 22. Comparing Impala to Hive ● Hive: MapReduce as an execution engine ● High latency, low throughput queries ● Fault-tolerance model based on MapReduce's on-disk checkpointing; materializes all intermediate results ● Java runtime allows for easy late-binding of functionality: file formats and UDFs. ● Extensive layering imposes high runtime overhead ● Impala: ● direct, process-to-process data exchange ● no fault tolerance ● an execution engine designed for low runtime overhead
- 23. Comparing Impala to Hive ● Impala's performance advantage over Hive: no hard numbers, but ● Impala can get full disk throughput (~100MB/sec/disk); I/O-bound workloads often faster by 3-4x ● queries that require multiple map-reduce phases in Hive see a higher speedup ● queries that run against in-memory data see a higher speedup (observed up to 100x)
- 24. Impala Roadmap: GA – April 2013 ● New data formats: ● Avro ● Parquet ● Improved query execution: partitioned joins ● Further performance improvements ● Guidelines for production deployment: ● load balancing across impalad's ● resource isolation within MR cluster
- 25. Impala Roadmap: 2013 ● Additional SQL: ● UDFs ● SQL authorization and DDL ● ORDER BY without LIMIT ● window functions ● support for structured data types ● Improved HBase support: ● composite keys, complex types in columns, index nested-loop joins, INSERT/UPDATE/DELETE
- 26. Impala Roadmap: 2013 ● Runtime optimizations: ● straggler handling ● join order optimization ● improved cache management ● data collocation for improved join performance ● Better metadata handling: ● automatic metadata distribution through statestore ● Resource management: ● goal: run exploratory and production workloads in same cluster, against same data, w/o impacting production jobs
- 27. Try it out! ● Beta version available since 10/24/12 ● Latest version is 0.6 ● We have packages for: ● RHEL 6.2/5.7 ● Ubuntu 10.04 and 12.04 ● SLES 11 ● Debian 6 ● We are targeting GA for April 2013 ● Questions/comments? impala-user@cloudera.org ● My email address: mgrover@cloudera.com ● My twitter handle: mark_grover