Thanks.
My test bed has the following components. 1. Spark version 1.5.2 2. Hive version 1.2.1 3. Hadoop version 2.6 I will try your suggestions, however, we have to consider that the underlying table is based on a Hive table, to keep the systematics the same so to speak for comparison. I increased the rows in table tt to 56 million and this is what a simple query compares 10 seconds in spark compared to 143 seconds with HIVE/MR. In spark-sql spark-sql> select count(1) from tt where object_id > 1000 and object_type = 'TABLE'; 2251008 Time taken: 10.295 seconds, Fetched 1 row(s) In hive with standard MR (no TEZ) hive> select count(1) from tt where object_id > 1000 and object_type = 'TABLE'; Query ID = hduser_20151201202549_b698c002-6de0-4353-9a4a-3ba06e7c0428 Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Starting Job = job_1448969636093_0004, Tracking URL = http://rhes564:8088/proxy/application_1448969636093_0004/ Kill Command = /home/hduser/hadoop-2.6.0/bin/hadoop job -kill job_1448969636093_0004 Hadoop job information for Stage-1: number of mappers: 11; number of reducers: 1 2015-12-01 20:25:58,765 Stage-1 map = 0%, reduce = 0% 2015-12-01 20:26:08,140 Stage-1 map = 5%, reduce = 0%, Cumulative CPU 6.38 sec 2015-12-01 20:26:10,201 Stage-1 map = 9%, reduce = 0%, Cumulative CPU 7.5 sec 2015-12-01 20:26:19,539 Stage-1 map = 14%, reduce = 0%, Cumulative CPU 13.92 sec 2015-12-01 20:26:20,570 Stage-1 map = 18%, reduce = 0%, Cumulative CPU 15.07 sec 2015-12-01 20:26:30,870 Stage-1 map = 23%, reduce = 0%, Cumulative CPU 21.55 sec 2015-12-01 20:26:31,897 Stage-1 map = 27%, reduce = 0%, Cumulative CPU 22.7 sec 2015-12-01 20:26:42,191 Stage-1 map = 32%, reduce = 0%, Cumulative CPU 29.45 sec 2015-12-01 20:26:43,217 Stage-1 map = 36%, reduce = 0%, Cumulative CPU 30.41 sec 2015-12-01 20:26:52,465 Stage-1 map = 41%, reduce = 0%, Cumulative CPU 36.83 sec 2015-12-01 20:26:53,493 Stage-1 map = 45%, reduce = 0%, Cumulative CPU 37.78 sec 2015-12-01 20:27:04,778 Stage-1 map = 50%, reduce = 0%, Cumulative CPU 44.12 sec 2015-12-01 20:27:05,806 Stage-1 map = 55%, reduce = 0%, Cumulative CPU 45.1 sec 2015-12-01 20:27:17,126 Stage-1 map = 59%, reduce = 0%, Cumulative CPU 51.67 sec 2015-12-01 20:27:18,150 Stage-1 map = 64%, reduce = 0%, Cumulative CPU 52.75 sec 2015-12-01 20:27:28,424 Stage-1 map = 69%, reduce = 0%, Cumulative CPU 59.24 sec 2015-12-01 20:27:29,453 Stage-1 map = 73%, reduce = 0%, Cumulative CPU 60.2 sec 2015-12-01 20:27:40,805 Stage-1 map = 77%, reduce = 0%, Cumulative CPU 66.63 sec 2015-12-01 20:27:42,855 Stage-1 map = 82%, reduce = 0%, Cumulative CPU 68.53 sec 2015-12-01 20:27:54,156 Stage-1 map = 87%, reduce = 0%, Cumulative CPU 74.99 sec 2015-12-01 20:27:55,180 Stage-1 map = 91%, reduce = 0%, Cumulative CPU 76.15 sec 2015-12-01 20:28:05,483 Stage-1 map = 96%, reduce = 0%, Cumulative CPU 82.64 sec 2015-12-01 20:28:06,509 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 83.4 sec 2015-12-01 20:28:10,622 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 84.72 sec MapReduce Total cumulative CPU time: 1 minutes 24 seconds 720 msec Ended Job = job_1448969636093_0004 MapReduce Jobs Launched: Stage-Stage-1: Map: 11 Reduce: 1 Cumulative CPU: 84.72 sec HDFS Read: 56718493 HDFS Write: 8 SUCCESS Total MapReduce CPU Time Spent: 1 minutes 24 seconds 720 msec OK 2251008 Time taken: 143.452 seconds, Fetched: 1 row(s) Mich Talebzadeh Sybase ASE 15 Gold Medal Award 2008 A Winning Strategy: Running the most Critical Financial Data on ASE 15 <http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15", ISBN 978-0-9563693-0-7. co-author "Sybase Transact SQL Guidelines Best Practices", ISBN 978-0-9759693-0-4 Publications due shortly: Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8 Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one out shortly <http://talebzadehmich.wordpress.com/> http://talebzadehmich.wordpress.com NOTE: The information in this email is proprietary and confidential. This message is for the designated recipient only, if you are not the intended recipient, you should destroy it immediately. Any information in this message shall not be understood as given or endorsed by Peridale Technology Ltd, its subsidiaries or their employees, unless expressly so stated. It is the responsibility of the recipient to ensure that this email is virus free, therefore neither Peridale Ltd, its subsidiaries nor their employees accept any responsibility. From: Jörn Franke [mailto:[email protected]] Sent: 01 December 2015 20:38 To: [email protected] Subject: Re: Using spark in tandem with Hive You Should use TEZ (preferably >0.8 and a release of hive supporting it, because it has tez service which allows more lower latency queries) instead of mr to get the first query faster. The second query is probably faster in hive because you use statistics, which to my knowledge are not leveraged by Spark (only for broadcast joins). However you can still create statistics for each column by executing the same command you use for the table, but adding for columns to it (probably also not supported by Spark). You may get faster access if you do not mark the table as transactional (I am not sure if spark can handle them properly). I do not know your data, but you should check if you want to sort the data on certain columns. Probably the bloom filter on object_id is not necessary because this is covered by the storage index. Bloom filters are only available in Hive >= 1.2. There might be further optimizations (eg partitioning, increasing replication etc), but this would require more knowledge of the data. On 01 Dec 2015, at 19:20, Mich Talebzadeh <[email protected] <mailto:[email protected]> > wrote: The table was created in spark-sql as ORC table use asehadoop; drop table if exists tt; create table tt ( owner varchar(30) ,object_name varchar(30) ,subobject_name varchar(30) ,object_id bigint ,data_object_id bigint ,object_type varchar(19) ,created timestamp ,last_ddl_time timestamp ,timestamp varchar(19) ,status varchar(7) ,temporary2 varchar(1) ,generated varchar(1) ,secondary varchar(1) ,namespace bigint ,edition_name varchar(30) ,padding1 varchar(4000) ,padding2 varchar(3500) ,attribute varchar(32) ,op_type int ,op_time timestamp ) CLUSTERED BY (object_id) INTO 256 BUCKETS STORED AS ORC TBLPROPERTIES ( "orc.compress"="SNAPPY", "transactional"="true", "orc.create.index"="true", "orc.bloom.filter.columns"="object_id", "orc.bloom.filter.fpp"="0.05", "orc.stripe.size"="268435456", "orc.row.index.stride"="10000" ) ; show create table tt; INSERT INTO TABLE tt SELECT owner , object_name , subobject_name , object_id , data_object_id , object_type , cast(created AS timestamp) , cast(last_ddl_time AS timestamp) , timestamp , status , temporary2 , generated , secondary , namespace , edition_name , padding1 , padding2 , attribute , 1 , cast(from_unixtime(unix_timestamp()) AS timestamp) FROM t_staging ; And it was analysed ass below hive> analyze table tt compute statistics; Table asehadoop.tt stats: [numFiles=30, numRows=1767886, totalSize=88388380, rawDataSize=5984968162] OK Time taken: 0.241 seconds HTH Mich Talebzadeh Sybase ASE 15 Gold Medal Award 2008 A Winning Strategy: Running the most Critical Financial Data on ASE 15 <http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15", ISBN 978-0-9563693-0-7. co-author "Sybase Transact SQL Guidelines Best Practices", ISBN 978-0-9759693-0-4 Publications due shortly: Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8 Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one out shortly <http://talebzadehmich.wordpress.com/> http://talebzadehmich.wordpress.com NOTE: The information in this email is proprietary and confidential. This message is for the designated recipient only, if you are not the intended recipient, you should destroy it immediately. Any information in this message shall not be understood as given or endorsed by Peridale Technology Ltd, its subsidiaries or their employees, unless expressly so stated. It is the responsibility of the recipient to ensure that this email is virus free, therefore neither Peridale Ltd, its subsidiaries nor their employees accept any responsibility. From: Jörn Franke [mailto:[email protected]] Sent: 01 December 2015 16:58 To: [email protected] <mailto:[email protected]> Subject: Re: Using spark in tandem with Hive How did you create the tables? Do you have automated statistics activated in Hive? Btw mr is outdated as a Hive execution engine. Use TEZ (maybe wait for 0.8 for sub second queries ) or use Spark as an execution engine in Hive. On 01 Dec 2015, at 17:40, Mich Talebzadeh <[email protected] <mailto:[email protected]> > wrote: What if we decide to use spark with Hive. I look to hear similar views My test bed comprised 1. Spark version 1.5.2 2. Hive version 1.2.1 3. Hadoop version 2.6 I made Spark to use Hive metastore. So using spark-sql I can pretty do whatever one can do with HiveQL I created and populated an ORC table in spark-sql.. It took 90 seconds to create and populate the table with 1.7 million rows spark-sql> select count(1) from tt; 1767886 Time taken: 5.169 seconds, Fetched 1 row(s) Now let me try to do the said operation on the same table with HCL and MR hive> use asehadoop; OK Time taken: 0.639 seconds hive> select count(1) from tt; Query ID = hduser_20151201162717_e3102633-f501-413b-b9cb-384ac50880ac Total jobs = 1 Launching Job 1 out of 1 Number of reduce tasks determined at compile time: 1 In order to change the average load for a reducer (in bytes): set hive.exec.reducers.bytes.per.reducer=<number> In order to limit the maximum number of reducers: set hive.exec.reducers.max=<number> In order to set a constant number of reducers: set mapreduce.job.reduces=<number> Starting Job = job_1448969636093_0001, Tracking URL = http://rhes564:8088/proxy/application_1448969636093_0001/ Kill Command = /home/hduser/hadoop-2.6.0/bin/hadoop job -kill job_1448969636093_0001 Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1 2015-12-01 16:27:27,154 Stage-1 map = 0%, reduce = 0% 2015-12-01 16:27:35,427 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 2.65 sec 2015-12-01 16:27:41,611 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.71 sec MapReduce Total cumulative CPU time: 3 seconds 710 msec Ended Job = job_1448969636093_0001 MapReduce Jobs Launched: Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.71 sec HDFS Read: 520151 HDFS Write: 8 SUCCESS Total MapReduce CPU Time Spent: 3 seconds 710 msec OK 1767886 Time taken: 25.635 seconds, Fetched: 1 row(s) So 5 seconds in Spark versus 25 seconds in Hive On a point query Hive does not seem to return the correct timing? hive> select * from tt where data_object_id = 10; Time taken: 0.063 seconds, Fetched: 72 row(s) Whereas in Spark I get spark-sql> select * from tt where data_object_id = 10; Time taken: 9.002 seconds, Fetched 72 row(s) 9 seconds looks far more plausible to me than 0.063 seonds. Or in an unlikely event Spark returns elapsed time, whereas Hive returns execution time? Thanks Mich Talebzadeh Sybase ASE 15 Gold Medal Award 2008 A Winning Strategy: Running the most Critical Financial Data on ASE 15 <http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf> http://login.sybase.com/files/Product_Overviews/ASE-Winning-Strategy-091908.pdf Author of the books "A Practitioner’s Guide to Upgrading to Sybase ASE 15", ISBN 978-0-9563693-0-7. co-author "Sybase Transact SQL Guidelines Best Practices", ISBN 978-0-9759693-0-4 Publications due shortly: Complex Event Processing in Heterogeneous Environments, ISBN: 978-0-9563693-3-8 Oracle and Sybase, Concepts and Contrasts, ISBN: 978-0-9563693-1-4, volume one out shortly <http://talebzadehmich.wordpress.com/> http://talebzadehmich.wordpress.com NOTE: The information in this email is proprietary and confidential. This message is for the designated recipient only, if you are not the intended recipient, you should destroy it immediately. Any information in this message shall not be understood as given or endorsed by Peridale Technology Ltd, its subsidiaries or their employees, unless expressly so stated. It is the responsibility of the recipient to ensure that this email is virus free, therefore neither Peridale Ltd, its subsidiaries nor their employees accept any responsibility.
