Testing the Performance of PipelineDB for Real-Time Statistics on Virtual Machines

By Digoal

Background

PipelineDB is a type of streaming relational database developed based on PostgreSQL (PipelineDB 0.8.1 is developed based on PostgreSQL 9.4.4). This type of database features automatic processing of streaming data. Instead of raw data, PipelineDB only stores the processed data, so it is very suitable for common real-time streaming data processing scenarios.

Such scenarios include website traffic statistics, monitoring statistics for IT services, App Store access statistics, real-time statistics for IoT sensor data, and real-time statistics for logistics orders.

Introduction to PipelineDB

PipelineDB is based on, and is wire compatible with, PostgreSQL 9.4 and has added functionality including continuous SQL queries, probabilistic data structures, sliding windowing, and stream-table joins.

According to PostgreSQL’s website,

“PipelineDB’s fundamental abstraction is what is called a continuous view. These are much like regular SQL views, except that their defining SELECT queries can include streams as a source to read from. The most important property of continuous views is that they only store their output in the database. That output is then continuously updated incrementally as new data flows through streams, and raw stream data is discarded once all continuous views have read it.”

Streaming Statistics Example

Create a continuous view without defining a table, which is similar to NoSQL.

pipeline=# CREATE CONTINUOUS VIEW v0 AS SELECT COUNT(*) FROM stream;    
CREATE CONTINUOUS VIEW
pipeline=# CREATE CONTINUOUS VIEW v1 AS SELECT COUNT(*) FROM stream;
CREATE CONTINUOUS VIEW

Activate the continuous view.

pipeline=# ACTIVATE;  
ACTIVATE 2

Write data into streams.

pipeline=# INSERT INTO stream (x) VALUES (1);  
INSERT 0 1
pipeline=# SET stream_targets TO v0;
SET
pipeline=# INSERT INTO stream (x) VALUES (1);
INSERT 0 1
pipeline=# SET stream_targets TO DEFAULT;
SET
pipeline=# INSERT INTO stream (x) VALUES (1);
INSERT 0 1

If you do not want to receive the streaming data, stop the streams.

pipeline=# DEACTIVATE;  
DEACTIVATE 2

Query the continuous view.

pipeline=# SELECT count FROM v0;  
count
-------
3
(1 row)
pipeline=# SELECT count FROM v1;
count
-------
2
(1 row)
pipeline=#

How to Deploy PipelineDB

Install PipelineDB.

[root@digoal soft_bak]# rpm -ivh pipelinedb-0.8.1-centos6-x86_64.rpm   
Preparing... ########################################### [100%]
1:pipelinedb ########################################### [100%]
/sbin/ldconfig: /opt/gcc4.9.3/lib/libstdc++.so.6.0.20-gdb.py is not an ELF file - it has the wrong magic bytes at the start.

/sbin/ldconfig: /opt/gcc4.9.3/lib64/libstdc++.so.6.0.20-gdb.py is not an ELF file - it has the wrong magic bytes at the start.


____ _ ___ ____ ____
/ __ \(_)___ ___ / (_)___ ___ / __ \/ __ )
/ /_/ / / __ \/ _ \/ / / __ \/ _ \/ / / / __ |
/ ____/ / /_/ / __/ / / / / / __/ /_/ / /_/ /
/_/ /_/ .___/\___/_/_/_/ /_/\___/_____/_____/
/_/

PipelineDB successfully installed. To get started, initialize a
database directory:

pipeline-init -D <data directory>

where <data directory> is a nonexistent directory where you'd
like all of your database files to live.

You can find the PipelineDB documentation at:

http://docs.pipelinedb.com

Configure PipelineDB.

[root@digoal soft_bak]# cd /usr/lib/pipelinedb  
[root@digoal pipelinedb]# ll
total 16
drwxr-xr-x 2 root root 4096 Oct 15 10:47 bin
drwxr-xr-x 5 root root 4096 Oct 15 10:47 include
drwxr-xr-x 6 root root 4096 Oct 15 10:47 lib
drwxr-xr-x 4 root root 4096 Oct 15 10:47 share

[root@digoal pipelinedb]# useradd pdb
[root@digoal pipelinedb]# vi /home/pdb/.bash_profile
# add by digoal
export PS1="$USER@`/bin/hostname -s`-> "
export PGPORT=1953
export PGDATA=/data01/pg_root_1953
export LANG=en_US.utf8
export PGHOME=/usr/lib/pipelinedb
export LD_LIBRARY_PATH=$PGHOME/lib:/lib64:/usr/lib64:/usr/local/lib64:/lib:/usr/lib:/usr/local/lib:$LD_LIBRARY_PATH
export DATE=`date +"%Y%m%d%H%M"`
export PATH=$PGHOME/bin:$PATH:.
export MANPATH=$PGHOME/share/man:$MANPATH
export PGHOST=$PGDATA
export PGDATABASE=pipeline
export PGUSER=postgres
alias rm='rm -i'
alias ll='ls -lh'
unalias vi

[root@digoal pipelinedb]# mkdir /data01/pg_root_1953
[root@digoal pipelinedb]# chown pdb:pdb /data01/pg_root_1953
[root@digoal pipelinedb]# chmod 700 /data01/pg_root_1953

[root@digoal pipelinedb]# su - pdb
pdb@digoal-> which psql
/usr/lib/pipelinedb/bin/psql

Initialize the database.

pdb@digoal-> psql -V  
psql (PostgreSQL) 9.4.4

pdb@digoal-> cd /usr/lib/pipelinedb/bin/
pdb@digoal-> ll
total 13M
-rwxr-xr-x 1 root root 62K Sep 18 01:01 clusterdb
-rwxr-xr-x 1 root root 62K Sep 18 01:01 createdb
-rwxr-xr-x 1 root root 66K Sep 18 01:01 createlang
-rwxr-xr-x 1 root root 63K Sep 18 01:01 createuser
-rwxr-xr-x 1 root root 44K Sep 18 01:02 cs2cs
-rwxr-xr-x 1 root root 58K Sep 18 01:01 dropdb
-rwxr-xr-x 1 root root 66K Sep 18 01:01 droplang
-rwxr-xr-x 1 root root 58K Sep 18 01:01 dropuser
-rwxr-xr-x 1 root root 776K Sep 18 01:01 ecpg
-rwxr-xr-x 1 root root 28K Sep 18 00:57 gdaladdo
-rwxr-xr-x 1 root root 79K Sep 18 00:57 gdalbuildvrt
-rwxr-xr-x 1 root root 1.3K Sep 18 00:57 gdal-config
-rwxr-xr-x 1 root root 33K Sep 18 00:57 gdal_contour
-rwxr-xr-x 1 root root 188K Sep 18 00:57 gdaldem
-rwxr-xr-x 1 root root 74K Sep 18 00:57 gdalenhance
-rwxr-xr-x 1 root root 131K Sep 18 00:57 gdal_grid
-rwxr-xr-x 1 root root 83K Sep 18 00:57 gdalinfo
-rwxr-xr-x 1 root root 90K Sep 18 00:57 gdallocationinfo
-rwxr-xr-x 1 root root 42K Sep 18 00:57 gdalmanage
-rwxr-xr-x 1 root root 236K Sep 18 00:57 gdal_rasterize
-rwxr-xr-x 1 root root 25K Sep 18 00:57 gdalserver
-rwxr-xr-x 1 root root 77K Sep 18 00:57 gdalsrsinfo
-rwxr-xr-x 1 root root 49K Sep 18 00:57 gdaltindex
-rwxr-xr-x 1 root root 33K Sep 18 00:57 gdaltransform
-rwxr-xr-x 1 root root 158K Sep 18 00:57 gdal_translate
-rwxr-xr-x 1 root root 168K Sep 18 00:57 gdalwarp
-rwxr-xr-x 1 root root 41K Sep 18 01:02 geod
-rwxr-xr-x 1 root root 1.3K Sep 18 00:51 geos-config
lrwxrwxrwx 1 root root 4 Oct 15 10:47 invgeod -> geod
lrwxrwxrwx 1 root root 4 Oct 15 10:47 invproj -> proj
-rwxr-xr-x 1 root root 20K Sep 18 01:02 nad2bin
-rwxr-xr-x 1 root root 186K Sep 18 00:57 nearblack
-rwxr-xr-x 1 root root 374K Sep 18 00:57 ogr2ogr
-rwxr-xr-x 1 root root 77K Sep 18 00:57 ogrinfo
-rwxr-xr-x 1 root root 283K Sep 18 00:57 ogrlineref
-rwxr-xr-x 1 root root 47K Sep 18 00:57 ogrtindex
-rwxr-xr-x 1 root root 30K Sep 18 01:01 pg_config
-rwxr-xr-x 1 root root 30K Sep 18 01:01 pg_controldata
-rwxr-xr-x 1 root root 33K Sep 18 01:01 pg_isready
-rwxr-xr-x 1 root root 39K Sep 18 01:01 pg_resetxlog
-rwxr-xr-x 1 root root 183K Sep 18 01:02 pgsql2shp
lrwxrwxrwx 1 root root 4 Oct 15 10:47 pipeline -> psql
-rwxr-xr-x 1 root root 74K Sep 18 01:01 pipeline-basebackup
lrwxrwxrwx 1 root root 9 Oct 15 10:47 pipeline-config -> pg_config
-rwxr-xr-x 1 root root 44K Sep 18 01:01 pipeline-ctl
-rwxr-xr-x 1 root root 355K Sep 18 01:01 pipeline-dump
-rwxr-xr-x 1 root root 83K Sep 18 01:01 pipeline-dumpall
-rwxr-xr-x 1 root root 105K Sep 18 01:01 pipeline-init
-rwxr-xr-x 1 root root 50K Sep 18 01:01 pipeline-receivexlog
-rwxr-xr-x 1 root root 56K Sep 18 01:01 pipeline-recvlogical
-rwxr-xr-x 1 root root 153K Sep 18 01:01 pipeline-restore
-rwxr-xr-x 1 root root 6.2M Sep 18 01:01 pipeline-server
lrwxrwxrwx 1 root root 15 Oct 15 10:47 postmaster -> pipeline-server
-rwxr-xr-x 1 root root 49K Sep 18 01:02 proj
-rwxr-xr-x 1 root root 445K Sep 18 01:01 psql
-rwxr-xr-x 1 root root 439K Sep 18 01:02 raster2pgsql
-rwxr-xr-x 1 root root 62K Sep 18 01:01 reindexdb
-rwxr-xr-x 1 root root 181K Sep 18 01:02 shp2pgsql
-rwxr-xr-x 1 root root 27K Sep 18 00:57 testepsg
-rwxr-xr-x 1 root root 63K Sep 18 01:01 vacuumdb

pdb@digoal-> pipeline-init -D $PGDATA -U postgres -E UTF8 --locale=C -W
pdb@digoal-> cd $PGDATA
pdb@digoal-> ll
total 108K
drwx------ 5 pdb pdb 4.0K Oct 15 10:57 base
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 global
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_clog
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_dynshmem
-rw------- 1 pdb pdb 4.4K Oct 15 10:57 pg_hba.conf
-rw------- 1 pdb pdb 1.6K Oct 15 10:57 pg_ident.conf
drwx------ 4 pdb pdb 4.0K Oct 15 10:57 pg_logical
drwx------ 4 pdb pdb 4.0K Oct 15 10:57 pg_multixact
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_notify
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_replslot
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_serial
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_snapshots
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_stat
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_stat_tmp
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_subtrans
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_tblspc
drwx------ 2 pdb pdb 4.0K Oct 15 10:57 pg_twophase
-rw------- 1 pdb pdb 4 Oct 15 10:57 PG_VERSION
drwx------ 3 pdb pdb 4.0K Oct 15 10:57 pg_xlog
-rw------- 1 pdb pdb 88 Oct 15 10:57 pipelinedb.auto.conf
-rw------- 1 pdb pdb 23K Oct 15 10:57 pipelinedb.conf

Configure stream processing parameters, such as setting the memory size, enabling or disabling synchronization, or setting the merged batch and number of worker processes.

pipelinedb.conf  
#------------------------------------------------------------------------------
# CONTINUOUS VIEW OPTIONS
#------------------------------------------------------------------------------

# size of the buffer for storing unread stream tuples
#tuple_buffer_blocks = 128MB

# synchronization level for combiner commits; off, local, remote_write, or on
#continuous_query_combiner_synchronous_commit = off

# maximum amount of memory to use for combiner query executions
#continuous_query_combiner_work_mem = 256MB

# maximum memory to be used by the combiner for caching; this is independent
# of combiner_work_mem
#continuous_query_combiner_cache_mem = 32MB

# the default fillfactor to use for continuous views
#continuous_view_fillfactor = 50

# the time in milliseconds a continuous query process will wait for a batch
# to accumulate
# continuous_query_max_wait = 10

# the maximum number of events to accumulate before executing a continuous query
# plan on them
#continuous_query_batch_size = 10000

# the number of parallel continuous query combiner processes to use for
# each database
#continuous_query_num_combiners = 2

# the number of parallel continuous query worker processes to use for
# each database
#continuous_query_num_workers = 2

# allow direct changes to be made to materialization tables?
#continuous_query_materialization_table_updatable = off

# inserts into streams should be synchronous?
#synchronous_stream_insert = off

# continuous views that should be affected when writing to streams.
# it is string with comma separated values for continuous view names.
#stream_targets = ''

Activate the database. As you can see, the native database supports PostgreSQL.

pdb@digoal-> pipeline-ctl start  
pdb@digoal-> psql pipeline postgres
psql (9.4.4)
Type "help" for help.

pipeline=# \l
List of databases
Name | Owner | Encoding | Collate | Ctype | Access privileges
-----------+----------+----------+---------+-------+-----------------------
pipeline | postgres | UTF8 | C | C |
template0 | postgres | UTF8 | C | C | =c/postgres +
| | | | | postgres=CTc/postgres
template1 | postgres | UTF8 | C | C | =c/postgres +
| | | | | postgres=CTc/postgres
(3 rows)
pipeline=# \dx
List of installed extensions
Name | Version | Schema | Description
------------------+----------+------------+---------------------------------------------------------------------
plpgsql | 1.0 | pg_catalog | PL/pgSQL procedural language
postgis | 2.2.0dev | pg_catalog | PostGIS geometry, geography, and raster spatial types and functions
postgis_topology | 2.2.0dev | topology | PostGIS topology spatial types and functions
(3 rows)

Check which functions are added to PipelineDB. Some functions are added as plug-ins, such as PostGIS. Some functions can be used as references or used directly.

pipeline=# select proname from pg_proc order by oid desc;  
......
second
minute
hour
day
month
year
......
cmsketch_empty
tdigest_add
tdigest_empty
tdigest_empty
bloom_add
bloom_empty
bloom_empty
hll_add
hll_empty
hll_empty
......

Conduct Performance Testing on a Virtual Machine (VM) on Your Own Laptop

Create five continuous views. A continuous view is a view for which you do not need to create a base table.

CREATE CONTINUOUS VIEW v0 AS SELECT COUNT(*) FROM stream;   
CREATE CONTINUOUS VIEW v1 AS SELECT sum(x::int),count(*),avg(y::int) FROM stream;
CREATE CONTINUOUS VIEW v001 AS SELECT sum(x::int),count(*),avg(y::int) FROM stream1;
CREATE CONTINUOUS VIEW v002 AS SELECT sum(x::int),count(*),avg(y::int) FROM stream2;
CREATE CONTINUOUS VIEW v003 AS SELECT sum(x::int),count(*),avg(y::int) FROM stream3;

Activate stream statistics.

activate;

View the data dictionary.

select relname from pg_class where relkind='C';

Conduct the batch insert test.

pdb@digoal-> vi test.sql  
insert into stream(x,y,z) select generate_series(1,1000),1,1;
insert into stream1(x,y,z) select generate_series(1,1000),1,1;
insert into stream2(x,y,z) select generate_series(1,1000),1,1;
insert into stream3(x,y,z) select generate_series(1,1000),1,1;

The following provides the test result. Note that you must use “simple” or “extended” here. If you use “prepared”, only the last SQL statement takes effect. It is not clear yet whether this is a PipelineDB or pgbench bug.

pdb@digoal-> /opt/pgsql/bin/pgbench -M extended -n -r -f ./test.sql -P 1 -c 10 -j 10 -T 100000  
progress: 1.0 s, 133.8 tps, lat 68.279 ms stddev 58.444
progress: 2.0 s, 143.9 tps, lat 71.623 ms stddev 53.880
progress: 3.0 s, 149.5 tps, lat 66.452 ms stddev 49.727
progress: 4.0 s, 148.3 tps, lat 67.085 ms stddev 55.484
progress: 5.1 s, 145.7 tps, lat 68.624 ms stddev 67.795

About 0.58 million records are written to the database every second, and the statistics for five continuous views are compiled.

All these operations are completed in-memory, so the speed is very fast. PipelineDB uses the worker process to merge data. The execution result of the top command during stress testing is as follows:

top - 11:23:07 up  2:49,  4 users,  load average: 1.83, 3.08, 1.78  
Tasks: 177 total, 5 running, 172 sleeping, 0 stopped, 0 zombie
Cpu(s): 11.6%us, 15.0%sy, 10.3%ni, 63.0%id, 0.0%wa, 0.0%hi, 0.1%si, 0.0%st
Mem: 3916744k total, 605084k used, 3311660k free, 27872k buffers
Swap: 1048572k total, 0k used, 1048572k free, 401748k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
11469 pdb 25 5 405m 75m 67m R 52.9 2.0 1:56.45 pipeline: bgworker: worker0 [pipeline]
12246 pdb 20 0 400m 69m 67m S 14.3 1.8 0:10.55 pipeline: postgres pipeline [local] idle
12243 pdb 20 0 400m 69m 67m S 13.3 1.8 0:10.45 pipeline: postgres pipeline [local] idle
12248 pdb 20 0 400m 69m 67m S 13.3 1.8 0:10.40 pipeline: postgres pipeline [local] idle
12244 pdb 20 0 400m 69m 67m S 12.6 1.8 0:10.50 pipeline: postgres pipeline [local] idle
12237 pdb 20 0 400m 69m 67m R 12.3 1.8 0:10.52 pipeline: postgres pipeline [local] idle
12247 pdb 20 0 402m 70m 67m R 12.3 1.8 0:10.70 pipeline: postgres pipeline [local] idle
12245 pdb 20 0 401m 69m 67m S 12.0 1.8 0:10.78 pipeline: postgres pipeline [local] idle
12235 pdb 20 0 400m 69m 67m S 11.3 1.8 0:10.88 pipeline: postgres pipeline [local] idle
12239 pdb 20 0 400m 69m 67m S 11.0 1.8 0:10.79 pipeline: postgres pipeline [local] idle
12241 pdb 20 0 400m 69m 67m S 11.0 1.8 0:10.53 pipeline: postgres pipeline [local] idle
11466 pdb 20 0 119m 1480 908 R 5.3 0.0 0:58.39 pipeline: stats collector process
11468 pdb 25 5 401m 12m 9744 S 2.3 0.3 0:16.49 pipeline: bgworker: combiner0 [pipeline]
12228 pdb 20 0 678m 3408 884 S 2.3 0.1 0:02.36 /opt/pgsql/bin/pgbench -M extended -n -r -f ./test.sql -P 1 -c 10 -j 10 -T 100000
11464 pdb 20 0 398m 17m 16m S 1.7 0.4 0:10.47 pipeline: wal writer process
11459 pdb 20 0 398m 153m 153m S 0.0 4.0 0:00.37 /usr/lib/pipelinedb/bin/pipeline-server
11460 pdb 20 0 115m 852 424 S 0.0 0.0 0:00.02 pipeline: logger process
11462 pdb 20 0 398m 3336 2816 S 0.0 0.1 0:00.06 pipeline: checkpointer process
11463 pdb 20 0 398m 2080 1604 S 0.0 0.1 0:00.08 pipeline: writer process
11465 pdb 20 0 401m 4460 1184 S 0.0 0.1 0:00.33 pipeline: autovacuum launcher process
11467 pdb 20 0 398m 1992 1056 S 0.0 0.1 0:00.00 pipeline: continuous query scheduler process

pdb@digoal-> psql
psql (9.4.4)
Type "help" for help.
pipeline=# select * from v0;
count
---------
9732439
(1 row)

pipeline=# select * from v1;
sum | count | avg
------------+---------+------------------------
4923514276 | 9837585 | 1.00000000000000000000
(1 row)

pipeline=# select * from v001;
sum | count | avg
--------------+----------+------------------------
505023543131 | 11036501 | 1.00000000000000000000
(1 row)

pipeline=# select * from v002;
sum | count | avg
---------------+----------+------------------------
1005065536319 | 12119513 | 1.00000000000000000000
(1 row)

pipeline=# select * from v003;
sum | count | avg
-------------+----------+------------------------
14948355485 | 29867002 | 1.00000000000000000000
(1 row)

After one billion streaming data records are written, the database size is still only 13 MB. This is because the streaming data is located in the memory and discarded after being processed.

pipeline=# \l+  
List of databases
Name | Owner | Encoding | Collate | Ctype | Access privileges | Size | Tablespace | Description
-----------+----------+----------+---------+-------+-----------------------+-------+------------+--------------------------------------------
pipeline | postgres | UTF8 | C | C | | 13 MB | pg_default | default administrative connection database
template0 | postgres | UTF8 | C | C | =c/postgres +| 12 MB | pg_default | unmodifiable empty database
| | | | | postgres=CTc/postgres | | |
template1 | postgres | UTF8 | C | C | =c/postgres +| 12 MB | pg_default | default template for new databases
| | | | | postgres=CTc/postgres | | |
(3 rows)

If your application has a similar scenario, this is the best solution.

Data from a Test Conducted on the Physical Machine

The results of the test conducted on the E5–2650 are as follows.

In this test, 10 PipelineDB instances are deployed and the preceding cases are executed. About 6 million data records are processed per second.

Combined with LVS, HAproxy, or JDBC LB, you can conduct large-scale real-time processing. This also indicates that the single-server performance of PipelineDB still has a lot of room for improvement.

Earlier versions of PipelineDB could not fully utilize the CPU. In the latest version, you do not need to deploy multiple instances, as you need only one instance for the entire CPU.

References

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