blob: a860e481258f4e3f7a876e3b8fb292c77e007f63 [file] [log] [blame]
# List of keyspace.tables to diff
keyspace_tables:
- ks1.tb1
- ks1.tb2
- ks2.tb3
# This is how many parts we split the full token range in.
# Each of these splits is then compared between the clusters
splits: 10000
# Number of buckets - splits / buckets should be under 100k to avoid wide partitions when storing the metadata
buckets: 100
# global rate limit - this is how many q/s you think the target clusters can handle
rate_limit: 10000
# optional job id - if restarting a job, set the correct job_id here to avoid re-diffing old splits
# job_id: 4e2c6c6b-bed7-4c4e-bd4c-28bef89c3cef
# Fetch size to use for the query fetching the tokens in the cluster
token_scan_fetch_size: 1000
# Fetch size to use for the queries fetching the rows of each partition
partition_read_fetch_size: 1000
read_timeout_millis: 10000
reverse_read_probability: 0.5
consistency_level: ALL
metadata_options:
keyspace: cassandradiff
replication: "{'class':'SimpleStrategy', 'replication_factor':'1'}"
ttl: 31536000
should_init: true
cluster_config:
source:
impl: "org.apache.cassandra.diff.ContactPointsClusterProvider"
name: "local_test_1"
contact_points: "127.0.0.1"
port: "9042"
dc: "datacenter1"
target:
impl: "org.apache.cassandra.diff.ContactPointsClusterProvider"
name: "local_test_2"
contact_points: "127.0.0.1"
port: "9043"
dc: "datacenter1"
metadata:
impl: "org.apache.cassandra.diff.ContactPointsClusterProvider"
name: "local_test"
contact_points: "127.0.0.1"
port: "9042"
dc: "datacenter1"