migration from mysql to cassandra for millions of active users

How did we migrate data for millions of live users from MySQL to CassandraAndrey Panasyuk, @defascat

Plan

Use caseChallenges

a. Individualb. Corporate

Servers● Thousands of servers in prod● Java 8● Tomcat 7● Spring 3● Hibernate 3

Sharded MySQL. Current state

Sharded MySQL. Environment1. MySQL (Percona Server)2. Hardware configuration:

a. two Intel E2620v2 CPUb. 128GB RAMc. 12x800GB Intel SSD, RAID 10d. two 2Gb network interfaces (bonded)

MemcacheD1. Hibernate

a. Query Cacheb. Entity Cache

2. 100th of nodes3. ~100MBps per Memcache node

Sharded MySQL. Failover1. master2. co-master3. flogger4. archive

X4

Sharded MySQL. Approach1. Hibernate changes:

a. Patching 2nd level caching:i. +environmentii. -class version

b. More info to debug problemsc. Fixing bugs

2. Own implementation:a. FitbitTransactionalb. ManagedHibernateSession

3. Dynamic sharding concept (somewhat similar to C*)

Sharded MySQL. Data migrationSolution: vBucket

Sharded MySQL. Data migration

Migration (96 -> 152 shards):● vBuckets to move: 96579● 1 bucket migration time: 8 min● 10 bucketmover * 3 processes - 12 days

Sharded MySQL. Data migrationJob

● Setupa. Ensures vbuckets in read-only modeb. Waits for servers to reach consensus

● Executea. Triggers actions (dump, insert, etc.) on Bucketmoverb. Waits for actions to complete

● Wrap-upa. Updates shards for vbuckets, re-opens them for writesb. Advances jobs to next action

Sharded MySQL. Schema migration1. Locks during schema update

Solution: pt-online-schema-change + protobuf

Drawbacks:

1. Split between DML/DDL scripts 2. Binary format (additional data)3. Additional platform specific tool

message Meta {optional string name = 1;optional string intro = 2;...repeated string requiredFeatures = 32;

}

message Challenge {optional Meta meta = 1;...optional CWRace cw_race = 6;

}

Sharded MySQL. Development1. Job system across shards2. Use unsharded databases for lookup tables3. Do not forget about custom annotation

@PrimaryEntity(entityType = EntityType.SHARDED_ENTITY)

Query patterns1. Create challenge2. List challenges by user3. Get challenge team leaderboard by user4. Post a message5. List challenge messages6. Cheer a message

MySQL. Not a problem1. Knowledge Base2. Response Time

Our problems1. MySQL

a. Scalabilityb. Fault tolerancec. Schema migrationd. Locks

2. Infrastructure costa. MemcacheDb. Redis

C* expectations1. Scalability2. Quicker fault recovery3. Easier schema migration4. Lack of locks

Migration specifics1. Millions of real users in prod2. No downtime

Apache CassandraApache Cassandra is a free and open-source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients.

Setting cluster up1. Performance testing2. Monitoring3. Alerting4. Incremental repairs (CASSANDRA-9935)

C* tweaks1. ParNew+CMS -> G12. MaxPauseGCMillis = 200ms3. ParallelGCThreads and ConcGCThreads = 44. Compaction5. gc_grace_seconds = 0 (already big TTL for our data)

Create keyspaces/tables

1. Almost the same schema with Cassandra adjustments2. Data denormalization was required in several places

ID migration1. Create pseudo-random migration UUID based on BIGINT2. Thank API designers for using string as object ids.3. Make sure clients are ready for the new length of the id.4. Migrate API to UUID all over the place

DAO (Data Access Object)1. Create CassandraDAO with the same interface as HibernateDAO 2. Create ProxyAdapterDAO to control which implementation to select3. Create adapter implementation for each DAO with the same

interface as HibernateDAO

Enable shadow writes (percentage)1. Introduce environment specific settings for shadow writes2. Adjust ProxyAdapterDAO code to enable shadow writes by

percentage. Various implementations.3. Analyze performance (StatsD metrics for our code + Cassandra

metrics)

Migrate legacy data1. Create a new job to read/migrate data2. Process data in batches

Start shadow C* reads with validation1. Environment specific settings for data validation2. Adjust ProxyAdapterDAO code to enable simultaneous read from

MySQL and Cassandra3. Adjust ProxyAdapterDAO to be able to compare objects4. Logging & investigating data discrepancy.

Check validation issues1. Path

a. Fix code problemsb. Migrate affected challenges againc. Go to step 1

2. Duration: 1.5 month

Turn on read from C*1. Introduce C* return read percentage in the config settings2. Still do shadow MySQL reads and validations3. Increase percentage over time

Turn off writes to MySQL

Clean-up1. Adjust places which are not suitable for C* patterns like look

through all of the shards.2. Adjust adapters to get rid of Hibernate DAOs. Adapter hierarchy is

still presented3. Remove obsolete code4. Clean up MySQL database

Challenge Events Migration Example1. Previous attempts:

a. SWF + SQSb. MySQL + Job across all shards

2. Nowa. Complication due to C* as a queue performanceb. 16 threads across 1024 buckets

Code Redesign. Message cheer example1. Originally

a. Readb. Update BLOBc. Persist

2. Approacha. Update C* set as a single operation

Code Redesign. Life without transactions1. BATCH2. Some object as a single source of truth

Challenges C*. Current State1. Two datacenters2. 18 nodes3. Hardware

a. 24-core CPUb. 64 GB RAM

4. RF: 3

Results of migration

1. Significant improvement in persistence storage scalability & management (comparing to MySQL RDBMS)

2. Minimizing number of external points of failures3. Squashing Technical Debt4. Created a reusable migration module

Cassandra Inconveniences1. Lack of ACID transactions2. MultiDC scenarios require concious decisions for

QUORUM/LOCAL_QUORUM. 3. Data denormalization4. CQL vs SQL limitations5. Less readable IDs

Surprisingly not a big deal1. Lack of JOINs due to the model2. Lack of aggregation functions due to the model (we’re on 2.1 now)3. Eventual consistency4. IDs format change