Best Practices for Migrating your Data Warehouse to Amazon Redshift
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You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
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Best Practices for Migrating your Data Warehouse to Amazon Redshift
- 1. Best Practices for Migrating your Data Warehouse to Amazon Redshift Eric Ferreira, Principal Database Engineer, Amazon Redshift Mar 2017
- 2. Migrating your Data Warehouse Overview • Why Migrate • Customer Success Stories • Amazon Redshift History and Development • Cluster Architecture • Migration Best Practices • Migration Tools • Open Q&A
- 3. Are you a user ?
- 4. Why Migrate
- 5. 100x faster Scales from GBs to PBs Analyze data without storage constraints 10x cheaper Easy to provision and operate Higher productivity 10x faster No programming Standard interfaces and integration to leverage BI tools, machine learning, streaming Transactional database MPP database Hadoop Why Migrate to Amazon Redshift?
- 6. Selected Amazon Redshift Customers
- 7. Migration from Oracle @ Boingo Wireless 2000+ Commercial Wi-Fi locations 1 million+ Hotspots 90M+ ad engagements 100+ countries Legacy DW: Oracle 11g based DW Before migration Rapid data growth slowed analytics Mediocre IOPS, limited memory, vertical scaling Admin overhead Expensive (license, h/w, support) After migration 180x performance improvement 7x cost savings
- 8. 0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 Exadata SAP HANA Redshift $400,000 $300,000 $55,000 7,200 2,700 15 15 Query Performance Data Load Performance 1 year of data 1 million records Latencyinseconds RedshiftExisting System 7X cheaper than Oracle Exadata 180X faster than Oracle database Migration from Oracle @ Boingo Wireless
- 9. Migration from Greenplum @ NTT Docomo 68 million customers 10s of TBs per day of data across mobile network 6PB of total data (uncompressed) Data science for marketing operations, logistics etc. Legacy DW: Greenplum on-premises After migration: 125 node DS2.8XL cluster 4,500 vCPUs, 30TB RAM 6 PB uncompressed 10x faster analytic queries 50% reduction in time for new BI app. deployment Significantly less ops. overhead
- 10. Migration from SQL on Hadoop @ Yahoo Analytics for website/mobile events across multiple Yahoo properties On an average day 2B events 25M devices Before migration: Hive – Found it to be slow, hard to use, share and repeat After migration: 21 node DC1.8XL (SSD) 50TB compressed data 100x performance improvement Real-time insights Easier deployment and maintenance
- 11. Migration from SQL on Hadoop @ Yahoo 1 10 100 1000 10000 Count Distinct Devices Count All Events Filter Clauses Joins Seconds Amazon Redshift Impala
- 12. Evaluation Operations and optimization Perform PoC and Validation Design Migration Plan Migration, Integration and Validation Amazon Redshift Migration Process
- 13. ENGINE X Amazon Redshift ETL Scripts SQL in reports Adhoc. queries How to Migrate? Schema Conversion Database Migration Map data types Choose compression encoding, sort keys, distribution keys Generate and apply DDL Schema & Data Transformation Data Migration Convert SQL Code Bulk Load Capture updates Transformations Assess Gaps Stored Procedures Functions 1 2 3 4
- 14. If you forget everything else… • Lift-and-Shift is NOT an ideal approach • Depending where you are coming from, it is sure to fail • AWS has a rich ecosystem of solutions • Your final solution will use other AWS services • AWS Solution Architects, ProServ, and Partners can help
- 15. Amazon Redshift History & Development
- 16. Columnar MPP OLAP AWS IAMAmazon VPC Amazon SWF Amazon S3 AWS KMS Amazon Route 53 Amazon CloudWatch Amazon EC2 PostgreSQL Amazon Redshift
- 17. February 2013 February 2017 > 100 Significant Patches > 140 Significant Features
- 18. Amazon Redshift Cluster Architecture
- 19. Amazon Redshift Cluster Architecture Massively parallel, shared nothing Leader node – SQL endpoint – Stores metadata – Coordinates parallel SQL processing Compute nodes – Local, columnar storage – Executes queries in parallel – Load, backup, restore 10 GigE (HPC) Ingestion Backup Restore SQL Clients/BI Tools 128GB RAM 16TB disk 16 cores S3 / EMR / DynamoDB / SSH JDBC/ODBC 128GB RAM 16TB disk 16 coresCompute Node 128GB RAM 16TB disk 16 coresCompute Node 128GB RAM 16TB disk 16 coresCompute Node Leader Node
- 20. Designed for I/O Reduction Columnar storage Data compression Zone maps aid loc dt 1 SFO 2016-09-01 2 JFK 2016-09-14 3 SFO 2017-04-01 4 JFK 2017-05-14 • Accessing dt with row storage: – Need to read everything – Unnecessary I/O aid loc dt CREATE TABLE loft_migration ( aid INT --audience_id ,loc CHAR(3) --location ,dt DATE --date );
- 21. Designed for I/O Reduction Columnar storage Data compression Zone maps aid loc dt 1 SFO 2016-09-01 2 JFK 2016-09-14 3 SFO 2017-04-01 4 JFK 2017-05-14 • Accessing dt with columnar storage: – Only scan blocks for relevant column aid loc dt CREATE TABLE loft_migration ( aid INT --audience_id ,loc CHAR(3) --location ,dt DATE --date );
- 22. Designed for I/O Reduction Columnar storage Data compression Zone maps aid loc dt 1 SFO 2016-09-01 2 JFK 2016-09-14 3 SFO 2017-04-01 4 JFK 2017-05-14 • Columns grow and shrink independently • Effective compression ratios due to like data • Reduces storage requirements • Reduces I/O aid loc dt CREATE TABLE loft_migration ( aid INT ENCODE LZO ,loc CHAR(3) ENCODE BYTEDICT ,dt DATE ENCODE RUNLENGTH );
- 23. Designed for I/O Reduction Columnar storage Data compression Zone maps aid loc dt 1 SFO 2016-09-01 2 JFK 2016-09-14 3 SFO 2017-04-01 4 JFK 2017-05-14 aid loc dt CREATE TABLE loft_migration ( aid INT --audience_id ,loc CHAR(3) --location ,dt DATE --date ); • In-memory block metadata • Contains per-block MIN and MAX value • Effectively prunes blocks which cannot contain data for a given query • Eliminates unnecessary I/O
- 25. Terminology and Concepts: Slices A slice can be thought of like a “virtual compute node” – Unit of data partitioning – Parallel query processing Facts about slices: – Each compute node has either 2, 16, or 32 slices – Table rows are distributed to slices – A slice processes only its own data
- 26. Terminology and Concepts: Data Distribution • Distribution style is a table property which dictates how that table’s data is distributed throughout the cluster: • KEY: Value is hashed, same value goes to same location (slice) • ALL: Full table data goes to first slice of every node • EVEN: Round robin • Goals: • Distribute data evenly for parallel processing • Minimize data movement during query processing KEY ALL Node 1 Slice 1 Slice 2 Node 2 Slice 3 Slice 4 Node 1 Slice 1 Slice 2 Node 2 Slice 3 Slice 4 Node 1 Slice 1 Slice 2 Node 2 Slice 3 Slice 4 EVEN
- 27. DS2.8XL Compute Node Ingestion Throughput: – Each slice’s query processors can load one file at a time: Streaming decompression Parse Distribute Write Realizing only partial node usage as 6.25% of slices are active 0 2 4 6 8 10 12 141 3 5 7 9 11 13 15 Data Loading Best Practices
- 28. Use at least as many input files as there are slices in the cluster With 16 input files, all slices are working so you maximize throughput COPY continues to scale linearly as you add nodes 16 Input Files DS2.8XL Compute Node 0 2 4 6 8 10 12 141 3 5 7 9 11 13 15 Data Loading Best Practices Continued
- 29. Data Preparation Export Data from Source System – CSV Recommend (Delimiter '|') Be aware of UTF-8 varchar columns (UTF-8 take 4 bytes per char) Be aware of your NULL character (N) – GZIP Compress Files – Split Files (1MB – 1GB after gzip compression) Useful COPY Options for PoC Data – MAXERRORS – ACCEPTINVCHARS – NULL AS
- 30. Keep Columns as Narrow as Possible • Buffers allocated based on declared column width • Wider than needed columns mean memory is wasted • Fewer rows fit into memory; increased likelihood of queries spilling to disk • Check SVV_TABLE_INFO(max_varchar) • SELECT max(len(col)) FROM table
- 31. Amazon Redshift is a Data Warehouse Optimized for batch inserts – The time to insert a single row in Redshift is roughly the same as inserting 100,000 rows Updates are delete + insert of the row • Deletes mark rows for deletion Blocks are immutable – Minimum space used is one block per column, per slice
- 32. Auto Compression – Samples data automatically when COPY into an empty table Samples up to 100,000 rows and picks optimal encoding – Turn off Auto Compression for Staging Tables Bake encodings into your DDL or use CREATE TABLE (LIKE …) Analyze Compression – Data profile has changed – Run after changing sort key Column Compression
- 33. Raw encoding (RAW) Byte-dictionary (BYTEDICT) Delta encoding (DELTA / DELTA32K) Mostly encoding (MOSTLY8 / MOSTLY16 / MOSTLY32) Runlength encoding (RUNLENGTH) Text encoding (TEXT255 / TEXT32K) LZO encoding (LZO) Zstandard (ZSTD) Average: 2-4x Column (Compression) Encoding Types
- 34. Primary/Unique/Foreign Key Constraints Primary/Unique/Foreign Key constraints are NOT enforced – If you load data multiple times, Amazon Redshift won’t complain – If you declare primary keys in your DDL, the optimizer will expect the data to be unique Redshift optimizer uses declared constraints to pick optimal plan – In certain cases it can result in performance improvements
- 35. Benchmarking Tips • Verify tables are vacuumed and analyzed • Check SVV_TABLE_INFO (STATS_OFF, UNSORTED) • Vacuum & Analyze • https://github.com/awslabs/amazon-redshift-utils/tree/master/src/AnalyzeVacuumUtility • Verify column encodings • Check PG_TABLE_DEF • Re-encode tables • https://github.com/awslabs/amazon-redshift-utils/tree/master/src/ColumnEncodingUtility • Verify good distribution keys • SVV_TABLE_INFO (SKEW_ROWS)
- 36. Migration Tools
- 37. Convert schema in a few clicks Sources include Oracle, Teradata, Greenplum and Netezza Automatic schema optimization Converts application SQL code Detailed assessment report AWS Schema Conversion Tool (AWS SCT)
- 38. AWS Schema Conversion Tool
- 39. Start your first migration in few minutes Sources include: Aurora, Oracle, SQL Server, MySQL and PostgreSQL Bulk load and continuous replication Migrate a TB for $3 Fault tolerant (AWS DMS)
- 40. AWS DMS: Change Data Capture Replication instance Source Target Update t1 t2 t1 t2 Transactions Change apply after bulk load
- 41. For Large Data Transfers AWS Import/Export Snowball AWS Snowball Edge AWS Snowmobile
- 42. Data Integration Partners Data Integration Systems Integrators Amazon Redshift
- 44. Resources https://github.com/awslabs/amazon-redshift-utils https://github.com/awslabs/amazon-redshift-monitoring https://github.com/awslabs/amazon-redshift-udfs Admin scripts Collection of utilities for running diagnostics on your cluster Admin views Collection of utilities for managing your cluster, generating schema DDL, etc. ColumnEncodingUtility Gives you the ability to apply optimal column encoding to an established schema with data already loaded Amazon Redshift Engineering’s Advanced Table Design Playbook https://aws.amazon.com/blogs/big-data/amazon-redshift-engineerings-advanced-table-design- playbook-preamble-prerequisites-and-prioritization/