Look Ma! No more blobs

Transcript

1. Look Ma! No more blobs Aparna Chaudhary NoSQL matters, @Cologne

   Germany 2013

2. EMBRACE POLYGLOT PERSISTENCE! STOP RDBMS ABUSE! KNOW YOUR USE CASE

3. Parse Extract Store Read XML We don't do rocket science...

   Use Case Runtime support for document types Metadata definition provided at runtime Document type names - max 50 char Look up content based on metadata RA

4. Challenges Storage of up to one million documents of 10KB

   to 2GB per document type per year Write 1MB < x msec Retrieve 1MB < y msec ......and details RA But…the Numbers make it interesting...

5. How? File System MongoDB RDBMS JCR Document Management

6. if you want to store files, its logical to use

   file system. ain't it? File System ✓ Ease of Use ✓ No special skill-set ✓ Backup and Recovery ✓ It’s free!

7. How do I name them? Support for metadata storage? Performance

   with too many small files? Query - Administration? High Availability? Limitation on total number of files?

8. Relational database Integrity Consistency Durability Atomicity Joins Backups High Availability

   You name it, We have it! RDBMS Aggregations

9. RDBMS Developer’s Perspective

10. Challenge #1 RA We need runtime support for document type.

    RA We need runtime support for document type.

11. Challenge #1 DOC_1 DOC_2 DOC_3 DOC_4 DOC_5 DOC_6 Dynamic DDL

    Generation DOC_1 DOC_2 DOC_3 DOC_4 DOC_5 DOC_6 Dynamic DDL Generation

12. Challenge #1 String concatenations are ugly… DEV String concatenations are

    ugly… DEV

13. Challenge #1 Let's build a utility. DEV Let's build a

    utility. DEV

14. Challenge #1 More Work More Work

15. Challenge #2 RA Document type is 50 char long RA

    Document type is 50 char long

16. Challenge #2 TABLE NAME LIMITS Wait… SQL-92 says 128 Char

    ? We rule. Let's support only 30 char. TABLE NAME LIMITS Wait… SQL-92 says 128 Char ? We rule. Let's support only 30 char.

17. Challenge #2 DOC_TYPE_MAPPING Let's create a mapping table. DEV DOC_TYPE_MAPPING

    Let's create a mapping table. DEV

18. Challenge #2 Ugly unreadable table names! Ugly unreadable table names!

19. So...finally... Read XML Dynamic DDL generation Document Type Alias DocumentType

    Defined Yes No Extract Metadata Store Metadata Store Content Simple use case becomes complex...

20. Remember... Our Challenge QA Let's see if we are in

    spec for response time. Aah..what about performance now? DEV

21. MongoDB Document Based GridFS B-Tree Dynamic Schema JSON BSON Query

    Scalable http://www.10gen.com/presentations/storage-engine-internals Joins Complex Transaction

22. F1 F2 F3 F4 F5 ID1 ID2 ID3 ID4 ID5

    F1 F1 F1 F1 F2 F2 F3 F4 F5 F6 F2 F3 F4 F5 Fx F8 F3 F9 F7 Concepts Database Collection Collection Collection Collection Collection Collection Database Collection Collection Collection Collection Collection Collection Database Collection Collection Collection Collection Collection Collection Database Collection Collection Collection Collection Collection Collection Table = Collection Column = Field Row = Document Database = Database

23. GridFS MongoDB divides the large content into chunks Stores Metadata

    and Chunks separately http://docs.mongodb.org/manual/core/gridfs/

24. > mybucket.files { "_id" : ObjectId("514d5cb8c2e6ea4329646a5c"), "chunkSize" : NumberLong(262144), "length"

    : NumberLong(103015), "md5" : "34d29a163276accc7304bd69c5520e55", "filename" : "health_record_2.xml", "contentType" : application/xml, "uploadDate" : ISODate("2013-03-23T07:41:44.907Z"), "aliases" : null, "metadata" : { "fname" : "Aparna", "lname" : "Chaudhary","country" : "Netherlands" } } ObjectId - 12 Byte BSON: 4 Byte - Seconds since Epoch 3 Byte - Machine Id 2 Byte - Process Id 3 Byte - Counter

25. > mybucket.chunks { "_id" : ObjectId("514d5cb8c2e6ea4329646a5d"), "files_id" : ObjectId("514d5cb8c2e6ea4329646a5c"), "n"

    : 0, "data" : BinData(0,...) }

26. ? I'm storing 10KB file, but would it use 256KB

    on disk? Last Chunk = FileSize % 256 + Metadata overhead 256 1128KB 256 256 256 104 + x 10KB 10 + x Chunk is as big as it needs to be...

27. Challenge #1 DEV MongoDB supports Dynamic Schema. You can use

    collection per docType and they are created dynamically. RA We need runtime support for document type.

28. Challenge #2 RA Document type is 50 char long DEV

    MongoDB namespace can be up to 123 char.

29. So...finally... Simple use case remains simple...well becomes simpler... Read XML

    Extract Metadata Store Metadata & Content

30. Remember... Our Challenge QA Let's see if we are in

    spec for response time. DEV Performance test is part of our definition of 'DONE'

31. BEcause seeing is believing! Demo ‣ GridFS 2.4.0 ‣ PostgreSQL

    9.2 ‣ Spring Data ‣ JMeter 2.7 ‣ Mac OS X 10.8.3 2.3GHz Quad-Core Intel Core i7, 16GB RAM https://github.com/aparnachaudhary/nosql-matters-demo

32. EMBRACE POLYGLOT PERSISTENCE! STOP RDBMS ABUSE! KNOW YOUR USE CASE

    @aparnachaudhary

33. Java Developer, Data Lover Eindhoven, Netherlands http://blog.aparnachaudhary.com/ @aparnachaudhary Thank You!