All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
auth.conf.factory |
DefaultAuthConfFactory | Name of a Scala module or class implementing AuthConfFactory providing custom authentication configuration |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
connection.compression |
Compression to use (LZ4, SNAPPY or NONE) | |
connection.factory |
DefaultConnectionFactory | Name of a Scala module or class implementing CassandraConnectionFactory providing connections to the Cassandra cluster |
connection.host |
localhost | Contact point to connect to the Cassandra cluster. A comma seperated list may also be used. ("127.0.0.1,192.168.0.1") |
connection.keep_alive_ms |
5000 | Period of time to keep unused connections open |
connection.local_dc |
None | The local DC to connect to (other nodes will be ignored) |
connection.port |
9042 | Cassandra native connection port |
connection.reconnection_delay_ms.max |
60000 | Maximum period of time to wait before reconnecting to a dead node |
connection.reconnection_delay_ms.min |
1000 | Minimum period of time to wait before reconnecting to a dead node |
connection.timeout_ms |
5000 | Maximum period of time to attempt connecting to a node |
query.retry.count |
10 | Number of times to retry a timed-out query |
query.retry.delay |
4 * 1.5 | The delay between subsequent retries (can be constant, like 1000; linearly increasing, like 1000+100; or exponential, like 1000*2) |
read.timeout_ms |
120000 | Maximum period of time to wait for a read to return |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
sql.pushdown.additionalClasses |
A comma seperated list of classes to be used (in order) to apply additional pushdown rules for C* Dataframes. Classes must implement CassandraPredicateRules | |
table.size.in.bytes |
None | Used by DataFrames Internally, will be updated in a future release to retrieve size from C*. Can be set manually now |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
sql.cluster |
default | Sets the default Cluster to inherit configuration from |
sql.keyspace |
None | Sets the default keyspace |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
connection.ssl.enabled |
false | Enable secure connection to Cassandra cluster |
connection.ssl.enabledAlgorithms |
Set(TLS_RSA_WITH_AES_128_CBC_SHA, TLS_RSA_WITH_AES_256_CBC_SHA) | SSL cipher suites |
connection.ssl.protocol |
TLS | SSL protocol |
connection.ssl.trustStore.password |
None | Trust store password |
connection.ssl.trustStore.path |
None | Path for the trust store being used |
connection.ssl.trustStore.type |
JKS | Trust store type |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
dev.customFromDriver |
None | Provides an additional class implementing CustomDriverConverter for those clients that need to read non-standard primitive Cassandra types. If your C* implementation uses a Java Driver which can read DataType.custom() you may need it this. If you are using OSS Cassandra this should never be used. |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
input.consistency.level |
LOCAL_ONE | Consistency level to use when reading |
input.fetch.size_in_rows |
1000 | Number of CQL rows fetched per driver request |
input.join.throughput_query_per_sec |
9223372036854775807 | Maximum read throughput allowed per single core in query/s while joining RDD with C* table |
input.metrics |
true | Sets whether to record connector specific metrics on write |
input.split.size_in_mb |
64 | Approx amount of data to be fetched into a Spark partition |
All parameters should be prefixed with spark.cassandra.
| Property Name | Default | Description |
|---|---|---|
output.batch.grouping.buffer.size |
1000 | How many batches per single Spark task can be stored in memory before sending to Cassandra |
output.batch.grouping.key |
Partition | Determines how insert statements are grouped into batches. Available values are
|
output.batch.size.bytes |
1024 | Maximum total size of the batch in bytes. Overridden by spark.cassandra.output.batch.size.rows |
output.batch.size.rows |
None | Number of rows per single batch. The default is 'auto' which means the connector will adjust the number of rows based on the amount of data in each row |
output.concurrent.writes |
5 | Maximum number of batches executed in parallel by a single Spark task |
output.consistency.level |
LOCAL_QUORUM | Consistency level for writing |
output.ifNotExists |
false | Determines that the INSERT operation is not performed if a row with the same primary key already exists. Using the feature incurs a performance hit. |
output.ignoreNulls |
false | In Cassandra >= 2.2 null values can be left as unset in bound statements. Setting this to true will cause all null values to be left as unset rather than bound. For finer control see the CassandraOption class |
output.metrics |
true | Sets whether to record connector specific metrics on write |
output.throughput_mb_per_sec |
2.147483647E9 | *(Floating points allowed)* Maximum write throughput allowed per single core in MB/s. Limit this on long (+8 hour) runs to 70% of your max throughput as seen on a smaller job for stability |