[Cassandra]在spark作业中的Cassandra sql中的行键级别进行筛选，导致cpu利用率过剩

Question

JavaRDD<CassandraRow> cassandraRDD = functions.cassandraTable("keyspace","column_family").
select("timeline_id","shopper_id","product_id").where("action=?", "Viewed")

CREATE TABLE keyspace.column_family (
    action text,
    timeline_id timeuuid,
    shopper_id text,
    product_id text,
    publisher_id text,
    referer text,
    remote_ip text,
    seed_product text,
    strategy text,
    user_agent text,
    PRIMARY KEY (action, timeline_id, shopper_id)
) WITH CLUSTERING ORDER BY (timeline_id DESC, shopper_id ASC)
    AND bloom_filter_fp_chance = 0.01
    AND caching = '{"keys":"ALL", "rows_per_partition":"NONE"}'
    AND comment = ''
    AND compaction = {'class': 'org.apache.cassandra.db.compaction.SizeTieredCompactionStrategy'}
    AND compression = {'sstable_compression': 'org.apache.cassandra.io.compress.LZ4Compressor'}
    AND dclocal_read_repair_chance = 0.1
    AND default_time_to_live = 0
    AND gc_grace_seconds = 864000
    AND max_index_interval = 2048
    AND memtable_flush_period_in_ms = 0
    AND min_index_interval = 128
    AND read_repair_chance = 0.0
    AND speculative_retry = '99.0PERCENTILE';

Answer 1

好吧，您这里有一个数据模型问题。action=分区键，因此所有类似的操作都存储在单个分区中=（一个节点+副本）。

你总共有多少不同的行动？你对热点的直觉是合理的。

您可能需要一个不同的分区键，或者需要向分区键添加一个额外的列，以便Cassandra在集群上均匀地分配数据。