Balancing Replication and Partitioning in a Distributed Java Database

In-memory databases offer significant gains in But three issues performance as all data is have stunted their freely available. There is no uptake: Address Traditional disk- spaces only being need to page to and from disk. oriented database large enough for a architecture is subset of a typical showing its age. This makes joins a user’s data. The ‘one problem. When data more bit’ problem must be joined across and durability. multiple machines performance degradation Snowflake is inevitable. Distributed in- Schemas allow memory databases us to mix But this model only goes solve these three Partitioning so far. “Connected problems but at the and Replication Replication” takes us a price of loosing the so joins never step further allowing us single address space. hit the wire. to make the best possible use of replication.

The lay of the land: The main architectural constructs in the database industry

ms μs ns ps 1MB Disk/Network 1MB Main Memory 0.000,000,000,000 Cross Continental Main Memory L1 Cache Ref Round Trip Ref Cross Network L2 Cache Ref Round Trip * L1 ref is about 2 clock cycles or 0.7ns. This is the time it takes light to travel 20cm

Taken from “OLTP Through the Looking Glass, and What We Found There” Harizopoulos et al

Shared Nothing Teradata, Vertica, Greenplumb… SN Regular In-Memory In-Memory Database Database Drop Disk Exasol, VoltDB, Oracle, Sybase, MySql Times Ten, HSQL, KDB Distribute Hana ODC Distributed Caching Coherence, Gemﬁre, Gigaspaces

Distributed Architecture Simplify the Contract. Stick to RAM

450 processes 2TB of RAM Oracle Coherence Messaging (Topic Based) as a system of record (persistence)

Access Layer Java Java client client API API Query Layer Transactions Data Layer Mtms Cashflows Persistence Layer

Indexing Partitioning Replication

But your storage is limited by the memory on a node

Keys Fs-Fz Keys Xa-Yd Scalable storage, bandwidth and processing

Trader Party Version 1 Trade Trader Party Version 2 Trade Trader Party Version 3 Trade Trader Party Version 4 Trade …and you need versioning to do MVCC

Trade Trader Party Party Trader Trade Party Trader Trade Party

So better to use partitioning, spreading data around the cluster.

Trader Party Trade Trade Trader Party

! This is what using Snowﬂake Schemas and the Connected Replication pattern is all about!

Replicated Trader Party Trade Partitioned

Valuation Legs Valuations art Transaction Mapping Cashflow Mapping Facts: Party Alias Transaction =>Big, Cashflows common Legs Parties keys Ledger Book Source Book Dimensions Cost Centre Product =>Small, Risk Organisation Unit Business Unit crosscutting HCS Entity Keys Set of Books 0 37,500,000 75,000,000 112,500,000 150,000,000

Coherence’s KeyAssociation gives us this Trades MTMs Common Key

Replicated Trader Party Trade Partitioned (

Query Layer Trader Party Trade Transactions Data Layer Mtms Cashflows Fact Storage (Partitioned)

Dimensions (repliacte) Transactions Mtms Facts Cashflows (distribute/ partition) Fact Storage (Partitioned)

Valuation Legs Valuations Facts: art Transaction Mapping Cashflow Mapping Party Alias =>Big =>Distribute Transaction Cashflows Legs Parties Ledger Book Source Book Dimensions =>Small Cost Centre Product Risk Organisation Unit => Replicate Business Unit HCS Entity Set of Books 0 37,500,000 75,000,000 112,500,000 150,000,000

We use a variant on a Snowﬂake Schema to partition big stuff, that has the same key and replicate small stuff that has crosscutting keys.

Select Transaction, MTM, ReferenceData From MTM, Transaction, Ref Where Cost Centre = ‘CC1’

Select Transaction, MTM, ReferenceData From MTM, Transaction, Ref Where Cost Centre = ‘CC1’ LBs[]=getLedgerBooksFor(CC1) SBs[]=getSourceBooksFor(LBs[]) So we have all the bottom level dimensions needed to query facts Transactions Mtms Cashflows Partitioned

Select Transaction, MTM, ReferenceData From MTM, Transaction, Ref Where Cost Centre = ‘CC1’ LBs[]=getLedgerBooksFor(CC1) SBs[]=getSourceBooksFor(LBs[]) So we have all the bottom level dimensions needed to query facts Transactions Get all Transactions and Mtms MTMs (cluster side join) for the passed Source Books Cashflows Partitioned

Select Transaction, MTM, ReferenceData From MTM, Transaction, Ref Where Cost Centre = ‘CC1’ Populate raw facts LBs[]=getLedgerBooksFor(CC1) (Transactions) with SBs[]=getSourceBooksFor(LBs[]) dimension data So we have all the bottom level before returning to dimensions needed to query facts client. Transactions Get all Transactions and Mtms MTMs (cluster side join) for the passed Source Books Cashflows Partitioned

Replicated Partitioned Java client Dimensions Facts API We never have to do a distributed join!

So all the big stuff is held paritioned And we can join without shipping keys around and having intermediate results

Trader Party Version 1 Trade Trader Party Version 2 Trade Trader Party Version 3 Trade Trader Party Version 4 Trade

Valuation Legs Valuations rt Transaction Mapping Cashflow Mapping Party Alias Facts Transaction Cashflows Legs Parties This is a dimension Ledger Book •  It has a different Source Book Cost Centre key to the Facts. Dimensions Product •  And it’s BIG Risk Organisation Unit Business Unit HCS Entity Set of Books 0 125,000,000

Party Alias Parties Ledger Book Source Book Cost Centre Product Risk Organisation Unit Business Unit HCS Entity Set of Books 0 1,250,000 2,500,000 3,750,000 5,000,000

Party Alias Parties Ledger Book Source Book Cost Centre Product Risk Organisation Unit Business Unit HCS Entity Set of Books 20 1,250,015 2,500,010 3,750,005 5,000,000

So we only replicate ‘Connected’ or ‘Used’ dimensions

Processing Layer Dimension Caches (Replicated) Transactions Data Layer As new Facts are added Mtms relevant Dimensions that they reference are moved Cashflows to processing layer caches Fact Storage (Partitioned)

Query Layer Save Trade (With connected dimension Caches) Data Layer Cache Trade (All Normalised) Store Partitioned Trigger Source Cache Party Ccy Alias Book

Query Layer (With connected dimension Caches) Data Layer Trade (All Normalised) Party Source Ccy Alias Book

Query Layer (With connected dimension Caches) Data Layer Trade (All Normalised) Party Source Ccy Alias Book Party Ledger Book

‘Connected Replication’ A simple pattern which recurses through the foreign keys in the domain model, ensuring only ‘Connected’ dimensions are replicated

Java client Java schema API Java ‘Stored Procedures’ and ‘Triggers’

Balancing Replication and Partitioning in a Distributed Java Database

Balancing Replication and Partitioning in a Distributed Java Database

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