I have 7 cassandra nodes (5 nodes with 32 cores and 32G memory, and 4 nodes with 4 cores and 64G memory), and deployed the spark workers on this cluster and spark's master was in the 8th node. And I used spark-cassandra-connector for them. Now my cassandra has almost 1 billion records with 30 fields, I write the scala including the following snippet:
def startOneCache(): DataFrame = {
val conf = new SparkConf(true)
.set("spark.cassandra.connection.host", "192.168.0.184")
.set("spark.cassandra.auth.username", "username")
.set("spark.cassandra.auth.password", "password")
.set("spark.driver.maxResultSize", "4G")
.set("spark.executor.memory", "12G")
.set("spark.cassandra.input.split.size_in_mb","64")
val sc = new SparkContext("spark://192.168.0.131:7077", "statistics", conf)
val cc = new CassandraSQLContext(sc)
val rdd: DataFrame = cc.sql("select user_id,col1,col2,col3,col4,col5,col6
,col7,col8 from user_center.users").limit(100000192)
val rdd_cache: DataFrame = rdd.cache()
rdd_cache.count()
return rdd_cache
}
In the spark's master I use spark-submit to run the above code,when executing the statement: rdd_cache.count(), I got a ERROR in the one worker node: 192.168.0.185:
16/03/08 15:38:57 INFO ShuffleBlockFetcherIterator: Started 4 remote fetches in 221 ms
16/03/08 15:43:49 WARN MemoryStore: Not enough space to cache rdd_6_0 in memory! (computed 4.6 GB so far)
16/03/08 15:43:49 INFO MemoryStore: Memory use = 61.9 KB (blocks) + 4.6 GB (scratch space shared across 1 tasks(s)) = 4.6 GB. Storage limit = 6.2 GB.
16/03/08 15:43:49 WARN CacheManager: Persisting partition rdd_6_0 to disk instead.
16/03/08 16:13:11 ERROR Executor: Managed memory leak detected; size = 4194304 bytes, TID = 24002
16/03/08 16:13:11 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 24002)
java.lang.IllegalArgumentException: Size exceeds Integer.MAX_VALUE
I simply thought the final error Size exceeds Integer.MAX_VALUE is caused by the warn: 16/03/08 15:43:49 WARN MemoryStore: Not enough space to cache rdd_6_0 in memory! (computed 4.6 GB so far) before it, but I don't know why, or whether I should set a larger than .set("spark.executor.memory", "12G") , what should I do for correcting this?