Real-time Analytics with Trino and Apache Pinot
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The document discusses real-time analytics using Trino and Apache Pinot, focusing on balancing latency and flexibility without sacrificing performance. It covers various ETL trade-offs like pre-joined, pre-aggregated, and pre-cubed tables, and highlights Apache Pinot's capabilities in handling high query loads and fast ingestion. It also details the Trino Pinot connector's functionalities, including aggregation pushdown and the configuration of data handling for optimized query performance.
![Ongoing and Future Work on the Connector
● Pinot Segments Deletion
● Table & Column Creation/Alter/Drop
CREATE TABLE DIMTABLE
(LONG_COL bigint, STRING_COL varchar)
WITH (
PRIMARY_KEY_COLUMNS = ARRAY['long_col'],
OFFLINE_CONFIG = '{
"tableName": "dimtable",
"tableType": "OFFLINE",
"isDimTable": true,
"segmentsConfig": {
"segmentPushType": "REFRESH",
"replication": "1"
},
"tenants": {
"broker": "DefaultTenant",
"server": "DefaultTenant"
},
"tableIndexConfig": {
"loadMode": "MMAP"
}
}’
);](https://image.slidesharecdn.com/trinosummit2021real-timeanalyticswithprestoandapachepinot-220208001605/85/Real-time-Analytics-with-Trino-and-Apache-Pinot-22-320.jpg)
Real-time Analytics with Trino and Apache Pinot
- 1. Real-time Analytics with Trino and Apache Pinot Xiang Fu, Elon Azoulay Oct 22, 2021
- 2. Speaker Intro Xiang Fu ● Co-founder - StarTree ● Previously: Streaming Platform @ Uber ● PMC & Committer: Apache Pinot Elon Azoulay ● Software Engineer - Stealth Mode Startup ● Previously: Data @ Facebook
- 3. Agenda ● Today’s Compromises: Latency vs. Flexibility ● Not trade-off using Apache Pinot ● Trino Pinot Connector ● Benchmark
- 4. Today’s Compromises: Latency vs Flexibility
- 5. Flexibility takes time: Join on the Fly customers orders customers.state = ‘California’ AND customers.gender = ‘Female’ JOIN customers ON (customers.customer_id = orders.customer_id) Group By customers.city, Month(orders.date) sum(orders.amount) FILTER JOIN GROUP BY AGGREGATION - Flexible to do any computation - High query cost: disk & network I/O, Data Partitioning, Data Serde
- 6. ETL Trade-offs: Pre-joined Table customers orders state = ‘California’ AND gender = ‘Female’ JOIN customers ON (customers.customer_id = orders.customer_id) Group By city, Month(orders. date) sum(amount) FILTER JOIN GROUP BY AGGREGATION user_orders _joined Pre-Joined Table - Flexible to explore user dimensions - Query time is still proportional to the data scan, not predictable
- 7. ETL Trade-offs: Pre-aggregated Table state = ‘California’ AND gender = ‘Female’ Group By city, Month(orders. date) sum(sum_amount) FILTER GROUP BY AGGREGATION user_orders _joined Pre-Joined Table user_orders_ aggregated Pre-Aggregated Table SELECT sum(amount) as sum_amount, date, city GROUP BY date, city Aggregation + GroupBy - Reduced query runtime workload - Query time is still proportional to the multiplication of non-groupBy columns
- 8. ETL Trade-offs: Pre-cubed Table state = ‘California’ AND gender = ‘Female’ sum_amount, month, city FILTER user_orders _joined Pre-Joined Table user_orders _cubed Pre-Aggregated Table SELECT sum(amount) as sum_amount, date, Month(date) as month, city GROUP BY CUBE (date, city, Month(date)) Cubing PROJECTION - Predictable query runtime - Storage overhead: one raw record translates to multiple records - Dimension explosion
- 9. Fact Table Dimension Table Pre-Join Pre-Aggregation Pre-Cube Latency Flexibility low high low high Not to Trade-off Using Apache Pinot Throughput high low
- 10. User Facing Applications Business Facing Metrics Apache Pinot Overview Anomaly Detection - Ingestion: Millions of events/sec - Workload: Thousands of queries/sec - Performance: Millisecond - Operation: Thousands Nodes Cluster ADLS GCS Real-Time Offline
- 11. BI Visualization Data Products Anomaly Detection ADLS GCS Real-Time Offline OLTP Server1 Server2 Server3 Zookeeper Broker 1 Broker 2 Controller
- 12. Secrets Behind Apache Pinot Scan Aggregation Filter Storage Bloom Filter Inverted Index Columnar Store Byte Encoding Sorted Index ❏Common Techniques ❏Pinot Compression Star-Tree Pre-aggregation Star- Tree Index Bit/RLE Encoding Per-segment flexible query planning Range Index Text Index
- 13. Apache Pinot - StarTree Index • Configurable trade-off between latency and space by partial pre-aggregation technique • Be able to achieve a hard upper bound for query latencies No pre-computation Latency Storage Full Pre-Cube (KV Store) Partial pre-computation (Startree Index) T= 10000 T= 100
- 14. Trino Overview Source: Trino Architecture
- 15. BI Visualization Data Products Anomaly Detection Ad hoc Analysis ADLS GCS Trino Pinot Connector
- 17. Trino Pinot Connector: Aggregation Pushdown Chasing the light: Aggregation pushdown - Issue single Pinot broker request - Best-effort push down for aggregations like count/sum/min/max/distinct/approximate_distinct, etc - 10~100x latency improvement
- 18. Passthrough Broker Queries SELECT CASE WHEN team = ‘Giants’ then ‘BIG’ else ‘SMALL’ END AS size, team, count(*) FROM pinot.default.”SELECT team FROM baseball_stats WHERE conference = ‘America East’” GROUP BY CASE WHEN team = ‘Giants’ then ‘BIG’ else ‘SMALL’ END, 2 Group by Expression Support
- 19. Passthrough Broker Queries SELECT team, count(*) FROM pinot.default.”SELECT team, player FROM baseball_stats WHERE conference = ‘America East’” ORDER BY CASE WHEN team = ‘Giants’ then ‘BIG’ else ‘SMALL’ END, 2 Order by Expression Support
- 20. Trino Pinot Connector: Server Query + Pinot Streaming API Pinot Streaming(gRpc) Connector - Distributed workload in parallel among Trino workers - Configurable memory footprint for data pulling from Pinot - Open the gate of queries requires full table scan or join
- 21. Ongoing and Future Work on the Connector ● Data Insertion ○ Push segments to the controller ○ Adds or replaces segments.
- 22. Ongoing and Future Work on the Connector ● Pinot Segments Deletion ● Table & Column Creation/Alter/Drop CREATE TABLE DIMTABLE (LONG_COL bigint, STRING_COL varchar) WITH ( PRIMARY_KEY_COLUMNS = ARRAY['long_col'], OFFLINE_CONFIG = '{ "tableName": "dimtable", "tableType": "OFFLINE", "isDimTable": true, "segmentsConfig": { "segmentPushType": "REFRESH", "replication": "1" }, "tenants": { "broker": "DefaultTenant", "server": "DefaultTenant" }, "tableIndexConfig": { "loadMode": "MMAP" } }’ );
- 23. Perf Benchmark Benchmark Config: - 1 Pinot Controllers (4 cores/8GB) - 1 Pinot Brokers (4 cores/8GB) - 3 Pinot Servers (4 cores/8GB) - 1 Trino Coordinator (4 cores/8GB) - 1 Trino Workers (4 cores/8GB) Data Set: - 40 Million rows data set Query: - Aggregation GroupBy + Predicate Trino: - Aggregation Pushdown enable/disable
- 24. Perf Benchmark Query Type: Aggregation Group By + Predicate pushdown
- 25. Thank you - Getting Started: https://tinyurl.com/trinoPinotTutorial - Run Trino in Kubernetes: https://github.com/trinodb/charts - StarTree: https://www.startree.ai/ - Apache Pinot: https://pinot.apache.org/ - Pinot on github: https://github.com/apache/pinot - Pinot slack: https://tinyurl.com/pinotSlackChannel - Apache Pinot Twitter: https://twitter.com/ApachePinot - Apache Pinot Meetup: https://www.meetup.com/apache-pinot - Starburst: https://www.starburst.io/ - Trino: https://trino.io/ - Trino on github: https://github.com/trinodb/trino - Trino slack: https://trino.io/slack.html
Editor's Notes
- #11: Realtime OLAP Database Columnar, Indexed Storage Low latency analytics Distributed – highly available, reliable, scalable Lambda architecture Offline data pushes Real-time stream ingestion Open Source
- #12: Explain controller - single coordinator controlling all actions cluster state - to maintain partition assignment - partition to server mapping Cluster state maintains start and end offsets
- #14: For certain query pattern (slice and dice on a given list of dimensions), we allow users to configure a upper bound of documents to scan. Pinot will intelligently partial pre-aggregate the records to achieve the requirement, but without exploding the storage
- #15: Components: Coordinator (query endpoint, metadata), workers (process query results) Divides the work into splits which are processed in parallel Results returned from coordinator in final phase of processing
- #17: Join 2 pinot tables
- #19: Build Broker Query Pushdown filter, aggregation, limit Produce a single broker split Submit broker request Produce Results to Trino Process joins, other filters, aggregations and final limit Return results to client
- #20: Build Broker Query Pushdown filter, aggregation, limit Produce a single broker split Submit broker request Produce Results to Trino Process joins, other filters, aggregations and final limit Return results to client
- #21: First step is to get the metadata from the pinot controller, Talk about how this is configurable with cache ttl config
- #24: Pinot - Fast single table OLAP Trino - Powerful connector ecosystem Complete system - covers entire landscape Get the best of Trino and Pinot Proven stack at Uber and many more
- #25: Pinot - Fast single table OLAP Trino - Powerful connector ecosystem Complete system - covers entire landscape Get the best of Trino and Pinot Proven stack at Uber and many more