Cassandra diff

Configuration

See spark-job/localconfig.yaml for an example config.

Custom cluster providers

To make it easy to run in any environment the cluster providers are pluggable - there are two interfaces to implement. First, the ClusterProvider interface is used to create a connection to the clusters, and it is configured using JobConfiguration#clusterConfig (see below).

cluster_config

This section has 3 parts - source, target and metadata where source and target describes the clusters that should be compared and metadata describes where we store information about any mismatches and the progress the job has done. Metadata can be stored in one of the source/target clusters or in a separate cluster.

The fields under source/target/metadata are passed in to the ClusterProvider (described by impl) as a map, so any custom cluster providers can be configured here.

Setting up clusters for diff

One way of setting up clusters for diff is to restore a snapshot to two different clusters and then modifying one of the clusters to be able to make sure that the queries still return the same results. This could include upgrades/replacements/bounces/decommission/expansion.

Environment variables

Currently usernames and passwords are set as environment variables when running the diff tool and the api server:

• diff.cluster.<identifier>.cql_user - the user name to use
• diff.cluster.<identifier>.cql_password - password

where <identifier> should be source, target and metadata for the username/password combinations for the matching clusters in the configuration.

Example

This example starts two cassandra single-node clusters in docker, runs stress to populate them and then runs diff to make sure the data matches;

You need to have docker and spark setup.

$ git clone <wherever>/cassandra-diff.git
$ cd cassandra-diff
$ mvn package
$ docker run --name cas-src -d  -p 9042:9042 cassandra:3.0.18
$ docker run --name cas-tgt -d  -p 9043:9042 cassandra:latest
$ docker exec cas-src cassandra-stress write n=1k
$ docker exec cas-tgt cassandra-stress write n=1k
$ spark-submit --verbose --files ./spark-job/localconfig.yaml --class org.apache.cassandra.diff.DiffJob spark-job/target/spark-job-0.1-SNAPSHOT.jar localconfig.yaml
# ... logs
INFO  DiffJob:124 - FINISHED: {standard1=Matched Partitions - 1000, Mismatched Partitions - 0, Partition Errors - 0, Partitions Only In Source - 0, Partitions Only In Target - 0, Skipped Partitions - 0, Matched Rows - 1000, Matched Values - 6000, Mismatched Values - 0 }
## start api-server:
$ mvn install
$ cd api-server
$ mvn exec:java
$ curl -s localhost:8089/jobs/recent | python -mjson.tool
  [
      {
          "jobId": "99b8d556-07ed-4bfd-b978-7d9b7b2cc21a",
          "buckets": 100,
          "keyspace": "keyspace1",
          "tables": [
              "standard1"
          ],
          "sourceClusterName": "local_test_1",
          "sourceClusterDesc": "ContactPoints Cluster: name=name, dc=datacenter1, contact points= [127.0.0.1]",
          "targetClusterName": "local_test_2",
          "targetClusterDesc": "ContactPoints Cluster: name=name, dc=datacenter1, contact points= [127.0.0.1]",
          "tasks": 10000,
          "start": "2019-08-16T11:47:36.123Z"
      }
  ]
$ curl -s localhost:8089/jobs/99b8d556-07ed-4bfd-b978-7d9b7b2cc21a/results | python -mjson.tool
  [
      {
          "jobId": "99b8d556-07ed-4bfd-b978-7d9b7b2cc21a",
          "table": "standard1",
          "matchedPartitions": 1000,
          "mismatchedPartitions": 0,
          "matchedRows": 1000,
          "matchedValues": 6000,
          "mismatchedValues": 0,
          "onlyInSource": 0,
          "onlyInTarget": 0,
          "skippedPartitions": 0
      }
  ]