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Performance Comparison Testing of Hive, esProc, and Impala Part 1

Three data computing languages

Performance comparison within Hive, Impala and esProc in grouping

summarizing, and join computing.

Hardware environment

PC count: 4
CPU: Intel Core i5 2500 (4 cores)
RAM: 16G
HDD: 2T/7200 rpm
Ethernet adapter: 1000 M

Software environment

OS: CentOS6. 4
JDK: 1. 6
Hadoop/hdfs 2. 2. 0

Test Result

Hive  0. 11. 0
esProc 3. 1
Impala 1. 2. 0

Data sampling

1. Restart PC before every test
2. Print the start time in the log before executing task
3. Print the end time in the log after executing task
4. Subtract the starting time from the ending time as the reference result
5. Repeat the step 1-4 for three times, and get the average value of the reference result as the final result of the test of this round

Test scenario

In order to ensure the test data is typical and comparable, the three products must go through the same computing. The Hive or Impala is designed for the data warehouse, providing the SQL-like syntax as the only available syntax. By comparison, esProc is designed as the complex procedural computing script, but not the data warehouse. In other words, esProc does not provide the SQL -style syntax directly, and esProc script can achieve the result of SQL computing by simulating in a more convenient style. So, the test computation this time is the SQL-style grouping, summarizing, and join operations.

In this test report, we use the HDFS and Hive incorporated in CDH5.0beta, while not the Hadoop that issued separately. This is because the Hadoop deployment and setup is rather complex, and the testing environment can frequently go wrong. But it is comparatively easy for CDH. esProc is easy to setup with an installation package of dozens MBs.

esProc supports both HDFS and the much faster operations on local disks, while Hive or Impala only supports HDFS. In order to test the extreme performances of these three solutions, esProc use the local disk for test, and split the data into several files and distribute them on several machines in advance, while Hive or Impala uses HDFS.

Grouping and Summarizing Test for Narrow Table

Data sample:
Table name: p_narrow
Col. count: 11
Row count: 500 million rows
Space occupied if saving as text: 120. 6G.
Data structure: personid int,name string,sex int,cityid int,birthday int,degree int,col1 string,col2 int,col3 int,col4 int,col5 string
Test case:
1.1 col. to group & 1 col. to summarize
Hive: select personid%10000, sum(col3) from p_narrow group by personid%10000
esProc: The codes fall into 3 parts. They are respectively: Program of summary machine, main program for node machine, and subprogram for node machine.

 

 


Impala: select personid%10000, sum(col3) from p_narrow group by personid%10000

2. 1 col. to group & 4 col. to summarize

Hive: select personid%10, count(col1), max(col2), sum(col3), count(col5) from p_narrow group by personid%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid;count(cul1count): cul1count,max(cul2count): cul2count,sum(cul3sum): cul3sum,count(cul5): cul5count)
The main program for node machine in cell A5 is:
=A4. groups@o(personid: groups@o(personid: cu1count,max(col2count): cul2count,sum(col3sum): cul3sum,count(col5): cul5count)
The main program for node machine in cell A1 is:
=cursor. groups(personid%10000: personid; count(col1count): co1count, max(col2count): col2count, sum(col3sum): col3sum,count(col5): col5count)
Impala: select personid%10, count(col1), max(col2), sum(col3), count(col5) from p_narrow group by personid%10

3. 4 col. to group & 1 col. to summarize

Hive: select personid%10, cityid%10, birthdayid%10, col4%10 from p_narrow group by personid%10,cityid%10,birthdayid%10,col4%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; sum(cul3sum): cul3sum)
The main program for node machine in cell A5 is changed to:
=A4. groups@o(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; sum(col3sum): cul3sum)
The main program for node machine in cell A1 is changed to:
=cursor. groups(personid%10: personid, cityid%10: cityid, birthdayid%10: birthdayid, col4%10: col4; sum(col3sum): col3sum)
Impala: select personid%10, cityid%10, birthdayid%10, col4%10 from p_narrow group by personid%10,cityid%10,birthdayid%10,col4%10

4.4 col. to group & 4 col. to summarize

Hive: select personid%10, cityid%10, birthdayid%10, col4%10, count(col1), max(col2), sum(col3), count(col5) from p_narrow group by personid%10,cityid%10,birthdayid%10,col4%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; count(cul1count): cul1count,max(cul2count): cul2count,sum(cul3sum): cul3sum,count(cul5): cul5count)
The main program for node machine in cell A5 is changed to:
=A4. groups@o(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; count(col1count): cu1count,max(col2count): cul2count,sum(col3sum): cul3sum,count(col5): cul5count)
The main program for node machine in cell A1 is changed to:
=cursor. groups(personid%10: personid, cityid%10: cityid, birthdayid%10: birthdayid, col4%10: col4; count(col1count): co1count, max(col2count): col2count, sum(col3sum): col3sum, count(col5): col5count)
Impala: select personid%10, cityid%10, birthdayid%10, col4%10, count(col1), max(col2), sum(col3), count(col5) from p_narrow group by personid%10,cityid%10,birthdayid%10,col4%10
Test results:

Test results:


Grouping and summarizing test for wide table

Data sample:
Table name: p
Col. count: 106
Row count: 60 million
Space occupied if saving as text: 127. 9G.
Data structure: personid int,name string,sex int,cityid int,birthday int,degree int,col1 int,col2 int,col3 int,col4 int,col5 int,col6 int,col7 int,col8 int,col9 int,col10 int,col11 int,col12 int,col13 int,col14 int,col15 int,col16 int,col17 int,col18 int,col19 int,col20 int,col21 int,col22 int,col23 int,col24 int,col25 int,col26 int,col27 int,col28 int,col29 int,col30 int,col31 int,col32 int,col33 int,col34 int,col35 int,col36 int,col37 int,col38 int,col39 int,col40 int,col41 int,col42 int,col43 int,col44 int,col45 int,col46 int,col47 int,col48 int,col49 int,col50 int,col51 int,col52 int,col53 int,col54 int,col55 int,col56 int,col57 int,col58 int,col59 int,col60 int,col61 int,col62 int,col63 int,col64 int,col65 int,col66 int,col67 int,col68 int,col69 int,col70 int,col71 int,col72 int,col73 int,col74 int,col75 int,col76 int,col77 int,col78 int,col79 int,col80 int,col81 int,col82 int,col83 int,col84 string,col85 string,col86 string,col87 string,col88 string,col89 string,col90 string,col91 string,col92 string,col93 string,col94 string,col95 string,col96 string,col97 string,col98 string,col99 string,col100 string

Test case:
1.1 col. to group & 1 col. to summarize
Hive: select personid%10000, sum(col3) from p group by personid%10000
esProc: The codes can be divided into 3 parts. They are respectively: Program for summary machine, main program for node machine, and subprogram for node machine.

 

 


Impala: select personid%10000, sum(col3) from p group by personid%10000

2.1 col. to group & 4 col. to summarize

Hive: select personid%10, count(col1), max(col2), sum(col3), count(col5) from p group by personid%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid;count(cul1count): cul1count,max(cul2count): cul2count,sum(cul3sum): cul3sum,count(cul5): cul5count)
The main program for node machine in cell A5 is changed to:
=A4. groups@o(personid: personid;count(col1count): cu1count,max(col2count): cul2count,sum(col3sum): cul3sum,count(col5): cul5count)
The main program for node machine in cell A1 is changed to:
=cursor. groups(personid%10000: personid; count(col1count): co1count, max(col2count): col2count, sum(col3sum): col3sum,count(col5): col5count)
Impala: select personid%10, count(col1), max(col2), sum(col3), count(col5) from p group by personid%10

3.4 col. to group & 1 col. to summarize

Hive: select personid%10, cityid%10, birthdayid%10, col4%10 from p group by personid%10,cityid%10,birthdayid%10,col4%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; sum(cul3sum): cul3sum)
The main program for node machine in cell A5 is changed to:
=A4. groups@o(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; sum(col3sum): cul3sum)
The main program for node machine in cell A1 is changed to:
=cursor. groups(personid%10: personid, cityid%10: cityid, birthdayid%10: birthdayid, col4%10: col4; sum(col3sum): col3sum)
Impala: select personid%10, cityid%10, birthdayid%10, col4%10 from p group by personid%10,cityid%10,birthdayid%10,col4%10

4.4 col. to group & 4 col. to summarize

Hive: select personid%10, cityid%10, birthdayid%10, col4%10, count(col1), max(col2), sum(col3), count(col5) from p group by personid%10,cityid%10,birthdayid%10,col4%10
esProc: The program for summary machine in cell A4 is changed to:
=A3. groups(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; count(cul1count): cul1count,max(cul2count): cul2count,sum(cul3sum): cul3sum,count(cul5): cul5count)
The main program for node machine in cell A5 is changed to:
=A4. groups@o(personid: personid, cityid: cityid, birthdayid: birthdayid, col4: col4; count(col1count): cu1count,max(col2count): cul2count,sum(col3sum): cul3sum,count(col5): cul5count)
The main program for node machine in cell A1 is changed to:
=cursor. groups(personid%10: personid, cityid%10: cityid, birthdayid%10: birthdayid, col4%10: col4; count(col1count): co1count, max(col2count): col2count, sum(col3sum): col3sum, count(col5): col5count)
Impala: select personid%10, cityid%10, birthdayid%10, col4%10, count(col1), max(col2), sum(col3), count(col5) from p group by personid%10,cityid%10,birthdayid%10,col4%10
Test results:


The performance testing and result comparison regarding the join computing will be discussed in the next article: Performance Comparison Testing of Hive, esProc, and Impala Part 2.

Personal blog: http://www.datakeyword.blogspot.com/
Web: http://www.raqsoft.com/product-esproc

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Jessica Qiu is the editor of Raqsoft. She provides press releases for data computation and data analytics.

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