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Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
Honey I Shrunk the Database
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Honey I Shrunk the Database

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  • I am Vanessa Hurst and I lead Data and Analytics at Paperless Post, a customizable online stationery startup in New York. I studied Computer Science and Systems and Information Engineering at the University of Virginia. I have experience in databases ranging from a few hundred megabyte CMSes for non-profits to terabytes of financial data and high traffic consumer websites. I've worked in data processing, product development, and business intelligence. I am happy open-source convert and lone data wrangler in a land of web developers using Ruby on Rails.
  • Static Data
  • This may include external, legal regulations or internal regulations such as terms of service.Data protection can also include mitigating risk or proactively screening before data is even available.HIPAA RegulationsPCI ComplianceAPI Terms of Use
  • Any other reasons?
  • RequirementsSlice -- significantly less space, power, & memory in staging and dev environments, need smaller data setMutation -- protect user data in highly personal communications, prevent staging use of customer emailsDaily RefreshResourcesSource -- production server w/ample space, power, & memoryDestination -- weak, shared staging infrastructure across several servers, local machine development infrastructureExpertise -- flexible infrastructure automation tools, many application developers, limited DBA time
  • RequirementsSlice -- significantly less space, power, & memory in staging and dev environments, need smaller data setMutation -- protect user data in highly personal communications, prevent staging use of customer emailsDaily RefreshResourcesSource -- production server w/ample space, power, & memoryDestination -- weak, shared staging infrastructure across several servers, local machine development infrastructureExpertise -- flexible infrastructure automation tools, many application developers, limited DBA time
  • Quick vocabularyBackup & restore, trigger-based replication, there are plenty of options that are all straight forward, but don’t give you a lot of leeway on resources.
  • Most common case
  • If you’re doing Business Intelligence, you need a copy of your production database. Figure it out.
  • Vertical -- difficult to keep data valid & usable -- valid units of space are not always valid in an applicatione.g. WAL logs, Pages 1-16 => smaller, finite size, not usableHorizontal -- requires application logic, highly customized but usable e.g. Users with ids 1-50, Users who joined before July 4 Users who are admins, any SQL logic
  • Vertical -- difficult to keep data valid & usable -- valid units of space are not always valid in an applicatione.g. WAL logs, Pages 1-16 => smaller, finite size, not usableHorizontal -- requires application logic, highly customized but usable e.g. Users with ids 1-50, Users who joined before July 4 Users who are admins, any SQL logic
  • http://www.postgresql.org/docs/current/static/app-pgdump.htmlOptions to: DumpOIDs in case your app uses them Leave out ownership commands (if staging environments run as different users)
  • http://www.postgresql.org/docs/current/static/app-pgdump.htmlOptions to: DumpOIDs in case your app uses them Leave out ownership commands (if staging environments run as different users)
  • Static Data
  • Dedicated schema preserves all table, index, sequence names, etc
  • Only the build process is staging-specific, all other privileges and settings match production
  • Only the build process is staging-specific, all other privileges and settings match production
  • Only the build process is staging-specific, all other privileges and settings match production
  • Only the build process is staging-specific, all other privileges and settings match production
  • RequirementsSlice -- significantly less space, power, & memory in staging and dev environments, need smaller data setMutation -- protect user data in highly personal communications, prevent staging use of customer emailsDaily RefreshResourcesSource -- production server w/ample space, power, & memoryDestination -- weak, shared staging infrastructure across several servers, local machine development infrastructureExpertise -- flexible infrastructure automation tools, many application developers, limited DBA time
  • http://github.com/rtomayko/replicate
  • Transcript

    • 1. Honey, I Shrunk the Database
      For Test and Development Environments
      Vanessa Hurst
      Paperless Post
      Postgres Open, September 2011
    • 2.
    • 3. User Data
    • 4. Why Shrink?
      Accuracy
      You don’t truly know how your app will behave in production unless you use real data.
      Production data is the ultimate in accuracy.
    • 5. Why Shrink?
      Accuracy
      Freshness
      New data should be available regularly.
      Full database refreshes should be timely.
    • 6. Why Shrink?
      Accuracy
      Freshness
      Resource Limitations
      Staging and developer machines cannot handle production load.
    • 7. Why Shrink?
      Accuracy
      Freshness
      Resource Limitations
      Data Protection
      Limit spread of sensitive user or client data.
    • 8. Why Shrink?
      Accuracy
      Freshness
      Resource Limitations
      Data Protection
    • 9. Case Study: Paperless Post
      Requirements
      Freshness – Daily, On command for non-developers
      Shrinkage – Slices, Mutations
    • 10. Case Study: Paperless Post
      Requirements
      Freshness – Daily, On command for non-developers
      Shrinkage – Slices, Mutations
      Resources
      Source – extra disk space, RAM, and CPUs
      Destination – limited, often entirely un-optimized
      Development -- constrained DBA resources
    • 11. Shrink Strategies
      Copies
      Restored backups or live replicas of entire production database
    • 12. Shrink Strategies
      Copies
      Slices
      Select portions of exact data
    • 13. Shrink Strategies
      Copies
      Slices
      Mutations
      Sanitized, anonymized, or otherwise changed data
    • 14. Shrink Strategies
      Copies
      Slices
      Mutations
      Assumptions
      Seed databases, fixtures, test data
    • 15. Shrink Strategies
      Copies
      Slices
      Mutations
      Assumptions
    • 16. Slices
      Vertical Slice
      Difficult to obtain a valid, useful subset of data.
      Example: Include some entire tables, exclude others
    • 17. Slices
      Vertical Slice
      Difficult to obtain a valid, useful subset of data.
      Example: Include some entire tables, exclude others
      Horizontal Slice
      Difficult to write and maintain.
      Example: SQL or application code to determine subset of data
    • 18. PG Tools – Vertical Slice
      Flexibility at Source (Production)
      pg_dump
      Include data only [-a --data-only]
      Include table schema only [-s --schema-only]
      Select tables [-t table1 table2 --table table1 table2]
      Select schemas [-nschema --schema=schema]
      Exclude schemas [-N schema --exclude-schema=schema]
    • 19. PG Tools – Vertical Slice
      Flexibility at Destination (Staging, Development)
      pg_restore
      Include data only [-a --data-only]
      Select indexes [-iindex --index=index]
      Tune processing [-jnumber-of-jobs --jobs=number-of-jobs]
      Select schemas [-nschema --schema=schema]
      Select triggers[-T trigger --trigger=trigger]
      Exclude privileges [-x --no-privileges --no-acl]
    • 20.
    • 21. Mutations
      External Data Protection
      HIPAA Regulations
      PCI Compliance
      API Terms of Use
    • 22. Mutations
      External Data Protection
      HIPAA Regulations
      PCI Compliance
      API Terms of Use
      Internal Data Protection
      Protecting your users’ personal data
      Protecting your users from accidents, e.g. staging emails
      Your Terms of Service
    • 23. User Data
    • 24. Case Study: Paperless Post
      Composite Slice includingVertical Slice – All application object schemasVertical Slice – Entire tables of static contentHorizontal Slice – Subset of users and their dataMutation – Changed user email addresses
    • 25. Case Study: Paperless Post
      Composite Slice includingVertical Slice – All application object schemas
      pg_dump --clean --schema-only --schema public db-01 > slice.sql
    • 26. Case Study: Paperless Post
      Composite Slice includingVertical Slice – All application object schemas
      pg_dump --clean --schema-only --schema public db-01 > slice.sqlVertical Slice – Entire tables of static content
      pg_dump --data-only --schema public -t cards db-01 >> slice.sql
    • 27. Case Study: Paperless Post
      Composite Slice includingVertical Slice – All application object schemas
      pg_dump --clean --schema-only --schema public db-01 > slice.sqlVertical Slice – Entire tables of static content
      pg_dump --data-only --schema public -t cards db-01 >> slice.sql
      Horizontal Slice – Subset of users and their dataMutation – Changed user email addresses
    • 28. Case Study: Paperless Post
      CREATE SCHEMA staging;
    • 29. Case Study: Paperless Post
      Horizontal Slice
      Custom SQLSELECT * INTO staging.usersFROM usersWHERE EXISTS (subset of users);
    • 30. Case Study: Paperless Post
      Horizontal Slice
      Custom SQLSELECT * INTO staging.usersFROM usersWHERE EXISTS (subset of users);
      Dynamic relative to full data set or newly created sliceSELECT * INTO staging.stuffFROM stuffWHERE EXISTS (stuff per staging.users);
    • 31. Case Study: Paperless Post
      Horizontal Slice
      Custom SQL
      Dynamic relative to full data set or newly created slice
      Mutations
      Email Addresses
      Use regular expressions to clean non-admin addressese.g. dude@gmail.com => staging+dudegmailcom@paperlesspost.com
      Cached Data
      Clear cached short link from link-shortening API
    • 32. Case Study: Paperless Post
      Composite Slice includingVertical Slice – All application object schemas
      pg_dump --clean --schema-only --schema public db-01 > slice.sqlVertical Slice – Entire tables of static content
      pg_dump --data-only --schema public -t cards db-01 >> slice.sql
      Horizontal Slice – Subset of users and their dataMutation – Changed user email addresses
      pg_dump --data-only --schema staging db-01 >> slice.sql
    • 33. Case Study: Paperless Post
      Rebuild
      Prepare new database as standby
      Gracefully close connections
      Rotate by renaming databases
      Security
      Dedicated database build user
      Membership in application user role
      Application user role & privileges remain
    • 34. Case Study: Paperless Post
      Rebuild
      $ bzcat slice.sql.bz2 | psql db-new
      Staging schema has not been created, so all data loads to default schema
    • 35. Case Study: Paperless Post
      We hacked our rebuild by importing across schemas!
      Now our sequences are wrong, causing duplicate data errors every time we try to insert into tables.
    • 36. Secret Weapon
      --Updates all serial sequences for ID columns only
      BEGIN
      FOR table_record IN SELECT pc.relname FROM pg_class pc WHERE pc.relkind = 'r' AND EXISTS (SELECT 1 FROM pg_attribute pa WHERE pa.attname = 'id' AND pa.attrelid = pc.oid) LOOP
      table_name = table_record.relname::text;
      EXECUTE 'SELECT setval(pg_get_serial_sequence(' || quote_literal(table_name) || ', ' || quote_literal('id')::text || '), MAX(id)) FROM ' || table_name || '
      WHERE EXISTS (SELECT 1 FROM ' || table_name || ')';
      END LOOP;
    • 37. Case Study: Paperless Post
      Rebuild
      $ bzcat slice.sql.bz2 | psql db-new
      Staging schema has not been created, so all data loads to default schema
      echo “select 1 from update_id_sequences();” >> slice.sql
      Vacuum
      Reindex
    • 38. Case Study: Paperless Post
      Security
      Database build user
      CREATE DB privileges
      Member of Application user role
      Application user remains database owner
      Application user privileges remain limited
      Build only works in predetermined environments
    • 39. Case Study: Paperless Post
      Requirements
      Freshness – Daily, On command for non-developers
      Shrinkage – Slices, Mutations
      Resources
      Source – extra disk space, RAM, and CPUs
      Destination – limited, often entirely un-optimized
      Development -- constrained DBA resources
    • 40. Questions?
      Vanessa Hurst
      Paperless Post
      @DBNess
      Postgres Open, September 2011
    • 41. More Tools
      Copies -- LVMSnapshots
      See talk by Jon Erdman at PG Conf EU
      Great for all reads
      Data stays virtualized & doesn’t take up space until changed
      Ideal for DDL changes without actual data changes
    • 42. More Tools
      Copies, Slices-- pg_staging by dmitrihttp://github.com/dimitri/pg_staging
      Simple -- pauses pgbouncer & restores backup
      Efficient -- leverage bulk loading
      Flexible -- supports varying psql files
      Custom -- limited
      Slices -- replicate by rtomayko of Github http://github.com/rtomayko/replicate
      Simple - Preserves object relations via ActiveRecord
      Inefficient -- Creates text-based .dump
      Inflexible -- Corrupts id sequences on data insert
      Custom -- highly

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