问题
In Java Spark, I have a dataframe that has a 'bucket_timestamp' column, which represents the time of the bucket that the row belongs to.
I want to write the dataframe to a Cassandra DB. The data must be written to the DB with TTL. The TTL should be depended on the bucket timestamp - where each row's TTL should be calculated as ROW_TTL = CONST_TTL - (CurrentTime - bucket_timestamp)
, where CONST_TTL
is a constant TTL that I configured.
Currently I am writing to Cassandra with spark using a constant TTL, with the following code:
df.write().format("org.apache.spark.sql.cassandra")
.options(new HashMap<String, String>() {
{
put("keyspace", "key_space_name");
put("table, "table_name");
put("spark.cassandra.output.ttl, Long.toString(CONST_TTL)); // Should be depended on bucket_timestamp column
}
}).mode(SaveMode.Overwrite).save();
One possible way I thought about is - for each possible bucket_timestamp - filter the data according to timestamp, calculate the TTL and write filtered data to Cassandra. but this seems very non-efficient and not the spark way. Is there a way in Java Spark to provide a spark column as the TTL option, so that the TTL will differ for each row?
Solution should be working with Java and dataset< Row>: I encountered some solutions for performing this with RDD in scala, but didn't find a solution for using Java and dataframe.
Thanks!
回答1:
For DataFrame API there is no support for such functionality, yet... There is JIRA for it - https://datastax-oss.atlassian.net/browse/SPARKC-416, you can watch it to get notified when it's implemented...
So only choice that you have is to use RDD API as described in the @bartosz25's answer...
回答2:
From Spark-Cassandra connector options (https://github.com/datastax/spark-cassandra-connector/blob/v2.3.0/spark-cassandra-connector/src/main/java/com/datastax/spark/connector/japi/RDDAndDStreamCommonJavaFunctions.java) you can set the TTL as:
- constant value (
withConstantTTL
) - automatically resolved value (
withAutoTTL
) - column-based value (
withPerRowTTL
)
In your case you could try the last option and compute the TTL as a new column of the starting Dataset
with the rule you provided in the question.
For use case you can see the test here: https://github.com/datastax/spark-cassandra-connector/blob/master/spark-cassandra-connector/src/it/scala/com/datastax/spark/connector/writer/TableWriterSpec.scala#L612
来源:https://stackoverflow.com/questions/51134344/spark-writing-to-cassandra-with-varying-ttl