a Data Lakehouse? b) Presto and Hudi overview 2) Getting your hands dirty (90 mins) a) Set up Hudi on Presto (Nothing to do, ships out of the box) b) Write and Read data using Spark in HUDI format c) Apply inserts, updates and delete to HUDI d) Query HUDI data in S3 data with Presto e) Set up Data streamer utility to ingest data seamlessly (auto pilot) 3) Summary and Close Out (5 - 10 mins) 4
engine • Query in Place • Federated Querying • ANSI SQL Compliant • Designed ground up for fast analytic queries against data of any size • Originally developed at Facebook • Proven on petabytes of data • SQL-On-Anything • Federated pluggable architecture to support many connector • Opensource, hosted on github • https://github.com/prestodb 7
Uber in hypergrowth ❏ Moving from warehouse to lake ❏ HDFS/Cloud storage is immutable Problems ❏ Extremely poor ingest performance ❏ Wasteful reading/writing ❏ Zero concurrency control or ACID
A, B, C, D, E Update: A => A’, D => D’ Update: A’ => A”, E => E’, Insert: F commit time=0 commit time=1 commit time=2 A, B C, D E file1_t0.parquet file2_t0.parquet file3_t0.parquet A’, B C, D’ file1_t1.parquet file2_t1.parquet A”, B E’,F file1_t2.parquet file3_t2.parquet A,B,C,D,E A,B,C,D,E A’,B,C,D’,E A”,B,C,D’,E’,F A’,D’ A”,E’,F
- Write cost is not an issue, but need fast reads - Workload is fairly understood and not bursty - Bound by parquet ingestion performance - Simple to operate Choose Merge On Read if: - Need quick ingestion - Workload can be spiky or can change in pattern - Some operational chops - Both read optimized and real time
Consume from source Step 3: Apply any transformation Step 4: Ingest to Hudi DeltaStreamer Repeat Steps 1 to 5 Source: Kafka, S3, DFS based source, etc. A self managed ingestion tool for Hudi Hudi for Data lake
ingest data into Hudi - Checkpointing - Various connectors like Kafka, DFS Log Files, Upstream tables … - Fault tolerant and reliable (auto rollbacks) - Automatic cleaning and compaction. - Filtering & SQL based transformation - Multi table ingestion - Sync Once or Continuous Mode
Minutes. Managed cloud service: No installation and configuration. 2. Built for data teams of all experience level. 3. Moderate level of control of deployment without complexity. 4. Dedicated support from Presto experts.
▪ /etc/presto/jvm.config Many hidden parameters – difficult to tune Just the query engine ▪ No built-in catalog – users need to manage Hive metastore or AWS Glue ▪ No data lake S3 integration Poor out-of-box perf ▪ No tuning ▪ No high-performance indexing ▪ Basic optimizations for even for common queries 22
ACCESS BILLING & SUPPORT In-VPC Presto Clusters (Compute Plane) AD HOC CLUSTER 1 TEST CLUSTER 2 PROD CLUSTER N Glue S3 RDS Elasticsearch Ahana Cloud Account Ahana console oversees and manages every Presto cluster Customer Cloud Account In-VPC orchestration of Presto clusters, where metadata, monitoring, and data sources reside Ahana Cloud for Presto 23
CDC (Change Data Capture) - Oracle, MySQL, PostGres, MSSQL, SalesForce, MongoDB etc. all provide change log feeds i. Robust but complicated and systemically expensive ii. Streaming Changes iii. Can segregate Inserts, Updates, Deletes b. Simple / Not Full Fledged : Incremental Pulls based on “last modified date” or “Incremental Primary Keys” i. Not all tables have an auto-updated last modified date column ii. Polling based iii. No segregation of Inserts, Updates (Every record is an upsert) iv. Cannot identify Deletes unless they are logical deletions 26
-> table select * from catalog.schema.table; 2. Show catalogs: show catalogs; 3. Show schemas in a catalog: show schemas from <catalog>; 4. Show tables in a schema: show tables from <catalog>.<schema>;
Hudi tables using session property. ❏ Implement a new DirectoryLister for Hudi that uses HoodieTableFileSystemView to fetch data files. ❏ File system view is loaded once and cached.
and read optimized queries ❏ Recognizing Hudi file layout through native integration ❏ File listing based on Hudi table metadata ❏ New Hudi connector (merged on master) ❏ COW (Snapshot) and MOR (Read optimized) querying ❏ Open up more optimization opportunities ❏ E.g data skipping, multi-modal indexes 34
fetching - Uses HFile for faster scans - Eliminate recursive file listing operations - Pruning using column stats Clustering (Optimize data layout alongside ingestion) - Problem 1: faster ingestion -> smaller file sizes - Problem 2: data locality for query (e.g., by city) ≠ ingestion order (e.g., trips by time) - Auto sizing, reorg data, no compromise on ingestion - Fancy space curves able to show 90% reduction in # rows scanned 35
incremental change streams - Incremental query using Hudi connector - Scan only relevant files in “incremental query window” Data Skipping - Scan record ranges based on their keys’ prefixes - Locality pertaining to predicate column - Avoid reading full index - O(#predicate columns) vs O(#table columns) Write support with Presto 36
directly via Glue ETL / Spark you need Glue HUDI connector from the Marketplace (Free) https://aws.amazon.com/marketplace/pp/prodview-6r ofemcq6erku 2. Alternatively you can create HUDI tables in Glue using: a. Glue API b. AWS Glue Command Line : https://docs.aws.amazon.com/cli/latest/reference/glue/create-ta ble.html c. Athena Web Console 38
prestodb.slack.com Write a blog for prestodb.io! prestodb.io/blog Join the virtual meetup group & present! meetup.com/prestodb Contribute to the project! github.com/prestodb 42
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