2017-06-22 131 views
1

我正在使用presto来查询Cassandra记录,它需要大约8分钟来响应结果。需要改善响应时间。Presto Cassandra Slow performance slow

的Presto配置如下:

coordinator=true 
    node-scheduler.include-coordinator=false 
    http-server.http.port=8080 
    query.max-memory=5GB 
    query.max-memory-per-node=3GB 
    discovery-server.enabled=true 
    discovery.uri=http://URL:8080 
    task.max-worker-threads=10 
    task.concurrency=32 

    Worker : 4 

    coordinator=false 
    http-server.http.port=8080 
    query.max-memory=5GB 
    query.max-memory-per-node=2GB 
    discovery.uri=http://URL:8080 
    task.max-worker-threads=16 
    task.concurrency=32 

    Cassandra : 4 NODE 

片段2 成本:CPU1.98米,输入:17833912行(1.49GB),输出:13089502行(1.31GB)
ScanFilterProject [表=卡桑德拉:卡桑德拉:rasapp:raslog,originalConstraint =(( “bucketid”= CAST( '2017062113' 成本:96.12%,输入:23169736行(22.10MB),输出:17833912行(1.49GB),过滤:23.03%

如何提高响应时间仍然使用分区键哈哈约2300万条记录?采取

CREATE TABLE TEST.TEST_LOG (
    bucketId    varchar, 
    id     timeuuid, 
    transaction_id  varchar, 
    ras_transaction_id varchar, 
    msg_seq_id   int, 
    host_name    varchar, 
    matip_channel_id  varchar, 
    hth_id    varchar, 
    mq_id     varchar, 
    log_point    varchar, 
    entry_time   timestamp, 
    exit_time    timestamp, 
    source_carrier  varchar, 
    destination_carrier varchar, 
    source_dcs   varchar, 
    destination_dcs  varchar, 
    message_type   varchar, 
    message_direction  int, 
    error_code_business varchar, 
    exception_code  varchar, 
    exception_description varchar, 
    scenario    varchar, 
    created_date   timestamp, 
    huborcar    varchar, 
    noof_fanout   varchar, 
    flight_date   timestamp, 
    route_origin   varchar, 
    route_destination  varchar, 
    class_service   varchar, 
    no_of_seats   varchar, 
    ras_host    varchar, 
    cp_host    varchar, 
    PRIMARY KEY(bucketid, created_date, msg_seq_id,message_direction,scenario,source_dcs,exception_code,log_point,transaction_id,id) 
) WITH default_time_to_live = 2851200 and CLUSTERING ORDER BY (created_date ASC, msg_seq_id ASC,message_direction ASC,scenario ASC,source_dcs ASC,exception_code ASC,log_point ASC,transaction_id ASC,id ASC); 

查询

select 
transaction_id, 
message_direction, 
message_type, 
max(exception_code) as exception_code, 
min(entry_time) as min_entry, 
max(entry_time) as max_entry, 
min(exit_time) as min_exit, 
max(exit_time) as max_exit 
from TEST.TEST_LOG 
where bucketid='2017062113' 
and (
((msg_seq_id<=2 and message_type='PAOREQ' ) or 
(msg_seq_id>2 and message_type='PAORES' ))) 
group by transaction_id, 
message_direction, 
message_type 

时间:8分钟

感谢,

+0

仅使用Cassandra时查询需要多长时间?什么是您正在运行的查询和表模式(包括哪些列是分区/集群密钥)? –

+0

请检查,更新后 – Augustin

+0

只需卡桑德拉需要多少时间? –

回答

0

两样东西:0.180版本的Presto将包括不平等的下推谓词聚类键,这将帮助你的查询。另外,您的模式对于您正在运行的查询不起作用。在Cassandra中,最好a)查询特定的分区(你这么做),也可以按照你使用它们的顺序在集群键上拥有谓词(因为这是Cassandra使用的排序顺序)。如果您有主键(bucketid,message_type,msg_seq_id,...),您可能会看到更好的性能。

此外,Presto不会将聚合压入Cassandra(或任何连接器),因此如果您要聚合的数据量很大,并且您不需要Presto用于联合查询,则可能会在Cassandra中执行查询的速度更快。