timescaledb 集成 madlib

github 上有人提出了一个问题（2017 很早了），然后搜索timescaledb 的docs 文档，发现有
一片介绍的文章，所以尝试运行下
备注：环境使用虚拟机安装（没有使用docker madlib 的原因，实际上可以尝试基于timescaledb 的镜像改造）

安装madlib

这个可以参考madlib 官方文档，或者https://www.cnblogs.com/rongfengliang/p/10298159.html
因为timescaledb 是一个pg 的标准扩展，可以复用以前的安装方式

添加pg 10 repo

 
yum install https://download.postgresql.org/pub/repos/yum/10/redhat/rhel-7-x86_64/pgdg-centos10-10-2.noarch.rpm

安装pg python 基本包

yum -y install postgresql10-plpython supervisor
 

安装madlib 依赖包
注意python 版本,我使用python 2.7 安装时候失败了，修改为了python34

 
yum update -y && yum install -y \

                    git \

                    gcc \

                    wget \

                    postgresql10-devel \

                    openssl \

                    m4 \

                    vim \

                    flex \

                    bison \

                    graphviz \

                    java \

                    epel-release \

                    python34-devel

安装pip 包
默认一般是包含的

 
yum install -y python34-pip

pg_conf 配置(环境变量)

PATH="$PATH:/usr/pgsql-10/bin"
 
 

安装python 依赖（通过pip）

 pip3 install awscli pygresql paramiko --upgrade
 
 

安装apache-madlib

下载rpm 包

wget   https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.rpm

安装

yum install -y apache-madlib-1.15.1-bin-Linux.rpm

timescaledb 扩展安装

下载timescaledb pg 扩展

wget https://timescalereleases.blob.core.windows.net/rpm/timescaledb-1.1.1-postgresql-10-0.x86_64.rpm
 

安装

yum install -y timescaledb-1.1.1-postgresql-10-0.x86_64.rpm
 

初始化数据库

/usr/pgsql-10/bin/postgresql-10-setup initdb
 
 

配置pg 扩展
/var/lib/pgsql/10/data/postgresql.conf 文件

 
+ shared_preload_libraries = 'timescaledb'

修改pg_hba.conf 添加访问支持
之后修改之后，需要重启服务，systemctl restart postgresql-10

 
/var/lib/pgsql/10/data/pg_hba.conf

修改如下：

# "local" is for Unix domain socket connections only

local all all trust

# IPv4 local connections:

host all all 127.0.0.1/32 trust

# IPv6 local connections:

host all all ::1/128 trust

启动数据库

systemctl enable postgresql-10

systemctl start postgresql-10

注册madlib 扩展

下载&&安装madlib 扩展

下载rpm 包

wget https://dist.apache.org/repos/dist/release/madlib/1.15.1/apache-madlib-1.15.1-bin-Linux.rpm

安装

yum install -y apache-madlib-1.15.1-bin-Linux.rpm

/usr/local/madlib/bin/madpack -s madlib -p postgres -c postgres@localhost:5432/postgres install
 
 

效果

madpack.py: INFO : Detected PostgreSQL version 10.6.

madpack.py: INFO : *** Installing MADlib ***

madpack.py: INFO : MADlib tools version = 1.15.1 (/usr/local/madlib/Versions/1.15.1/bin/../madpack/madpack.py)

madpack.py: INFO : MADlib database version = None (host=localhost:5432, db=postgres, schema=madlib)

madpack.py: INFO : Testing PL/Python environment...

madpack.py: INFO : > Creating language PL/Python...

madpack.py: INFO : > PL/Python environment OK (version: 2.7.5)

madpack.py: INFO : > Preparing objects for the following modules:

madpack.py: INFO : > - array_ops

madpack.py: INFO : > - bayes

madpack.py: INFO : > - crf

madpack.py: INFO : > - elastic_net

madpack.py: INFO : > - linalg

madpack.py: INFO : > - pmml

madpack.py: INFO : > - prob

madpack.py: INFO : > - sketch

madpack.py: INFO : > - svec

madpack.py: INFO : > - svm

madpack.py: INFO : > - tsa

madpack.py: INFO : > - stemmer

madpack.py: INFO : > - conjugate_gradient

madpack.py: INFO : > - knn

madpack.py: INFO : > - lda

madpac

检查

/usr/local/madlib/bin/madpack -s madlib -p postgres -c postgres@localhost:5432/postgres install-check
 
 

效果

TEST CASE RESULT|Module: bayes|bayes.ic.sql_in|PASS|Time: 121 milliseconds

TEST CASE RESULT|Module: crf|crf_train_small.ic.sql_in|PASS|Time: 112 milliseconds

TEST CASE RESULT|Module: crf|crf_test_small.ic.sql_in|PASS|Time: 133 milliseconds

TEST CASE RESULT|Module: elastic_net|elastic_net.ic.sql_in|PASS|Time: 133 milliseconds

TEST CASE RESULT|Module: linalg|linalg.ic.sql_in|PASS|Time: 44 milliseconds

TEST CASE RESULT|Module: linalg|svd.ic.sql_in|PASS|Time: 168 milliseconds

TEST CASE RESULT|Module: linalg|matrix_ops.ic.sql_in|PASS|Time: 238 milliseconds

TEST CASE RESULT|Module: prob|prob.ic.sql_in|PASS|Time: 21 milliseconds

TEST CASE RES

简单测试

内容来自官方文档
https://docs.timescale.com/v0.11/tutorials/tutorial-forecasting,https://docs.timescale.com/v1.1/tutorials/tutorial-hello-nyc
同时需要安装的组件也比较多，gis，timescale。。

预备安装
gis 扩展安装,后边需要,实际上这个是可选的

wget https://download.postgresql.org/pub/repos/yum/10/redhat/rhel-7-x86_64/pgdg-centos10-10-2.noarch.rpm // 这个yum 源

上边实际上已经执行了

yum  install -y postgis25_10

加载扩展
都在postgres 数据库

 
psql -U postgres -d  postgres -h localhost 

\c postgres

CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;

CREATE EXTENSION postgis;

加载数据部分数据（因为gis 数据有依赖关系）
数据有点多，可能会比较慢

 
wget https://timescaledata.blob.core.windows.net/datasets/nyc_data.tar.gz

tar -xvzf nyc_data.tar.gz

psql -U postgres -d  postgres -h localhost < nyc_data.sql

psql -U postgres -d postgres -h localhost -c "\COPY rides FROM nyc_data_rides.csv CSV"

初始化gis 扩展 && forecast.sql
数据有点多，可能会比较慢

 
ALTER TABLE rides ADD COLUMN pickup_geom geometry(POINT,2163);

ALTER TABLE rides ADD COLUMN dropoff_geom geometry(POINT,2163);

生成geo 数据：

UPDATE rides SET pickup_geom = ST_Transform(ST_SetSRID(ST_MakePoint(pickup_longitude,pickup_latitude),4326),2163);

UPDATE rides SET dropoff_geom = ST_Transform(ST_SetSRID(ST_MakePoint(dropoff_longitude,dropoff_latitude),4326),2163);

wget http://assets.iobeam.com/sql/forecast.sql

​

// 此过程会创建rides_count rides_length rides_price  等hypertables 

psql -U postgres -d postgres -h localhost -f forecast.sql

查询操作

查询rides_price
如下：

 
SELECT * FROM rides_price;

      one_hour | trip_price

---------------------+------------------

 2016-01-01 00:00:00 | 58.34

 2016-01-01 01:00:00 | 58.34

 2016-01-01 02:00:00 | 58.34

 2016-01-01 03:00:00 | 58.34

 2016-01-01 04:00:00 | 58.34

 2016-01-01 05:00:00 | 59.59

 2016-01-01 06:00:00 | 58.34

 2016-01-01 07:00:00 | 60.3833333333333

 2016-01-01 08:00:00 | 61.2575

 2016-01-01 09:00:00 | 58.435

 2016-01-01 10:00:00 | 63.952

 2016-01-01 11:00:00 | 59.9576923076923

 2016-01-01 12:00:00 | 60.4

创建训练以及测试的数据集
这个主要是测试从肯尼迪国际机场到时代广场的车程价格

 
-- Make the training dataset

SELECT * INTO rides_price_train FROM rides_price

WHERE one_hour <= '2016-01-21 23:59:59';

​

-- Make the testing dataset

SELECT * INTO rides_price_test FROM rides_price

WHERE one_hour >= '2016-01-22 00:00:00';

效果

SELECT * INTO rides_price_train FROM rides_price

postgres-# WHERE one_hour <= '2016-01-21 23:59:59';

SELECT 504

postgres=# SELECT * INTO rides_price_test FROM rides_price

postgres-# WHERE one_hour >= '2016-01-22 00:00:00';

SELECT 240

使用madlib 函数生成训练数据

DROP TABLE IF EXISTS rides_price_output;

DROP TABLE IF EXISTS rides_price_output_residual;

DROP TABLE IF EXISTS rides_price_output_summary;

DROP TABLE IF EXISTS rides_price_forecast_output;

​

SELECT madlib.arima_train('rides_price_train', -- input table

      'rides_price_output', -- output table

      'one_hour', -- timestamp column

      'trip_price', -- time-series column

      NULL, -- grouping columns

      TRUE, -- include_mean

      ARRAY[2,1,3] -- non-seasonal orders

      );

​

SELECT madlib.arima_forecast('rides_price_output', -- model table

                        'rides_price_forecast_output', -- output table

                        240 -- steps_ahead (10 days)

                        );

查看生成的结果

 
    SELECT * FROM rides_price_forecast_output;

数据

 SELECT * FROM rides_price_forecast_output;

 steps_ahead | forecast_value

-------------+----------------

           1 | 62.317574661

           2 | 62.7126520761

           3 | 62.892038632

           4 | 62.755044625

           5 | 62.6064068106

           6 | 62.6197088752

           7 | 62.7032172965

           8 | 62.729257786

           9 | 62.6956015739

          10 | 62.6685762986

          11 | 62.6755999496

          12 | 62.6926055721

          13 | 62.695637064

          14 | 62.6878845777

          15 | 62.683

结论：从肯尼迪国际机场到时代广场的车程价格在日常基础上保持不变

模型评估的

ALTER TABLE rides_price_test ADD COLUMN id SERIAL PRIMARY KEY;

ALTER TABLE rides_price_test ADD COLUMN forecast DOUBLE PRECISION;

​

UPDATE rides_price_test

SET forecast = rides_price_forecast_output.forecast_value

FROM rides_price_forecast_output

WHERE rides_price_test.id = rides_price_forecast_output.steps_ahead;

​

SELECT madlib.mean_abs_perc_error('rides_price_test', 'rides_price_mean_abs_perc_error', 'trip_price', 'forecast');

​

SELECT * FROM rides_price_mean_abs_perc_error;

结果

SELECT * FROM rides_price_mean_abs_perc_error;

 mean_abs_perc_error

---------------------

  0.0423789161947618

结论：从机场到曼哈顿的旅行价格保持在62美元，并且与测试数据集相比表现良好

说明

有一些关于机器学习以及统计的专业术语不是很懂，但是从基本的运行上，说明，madlib 与timescaledb 的集成还是
很方便的，这样我们既能有时序数据库的方便，同时还包含了基于sql 的机器学习能力，当然我们同时也可以集成graphql
engine 进行方便的graphql api ，总的来说基于标准pg 扩展的模型，部署还挺方便的

参考资料

https://docs.timescale.com/v0.11/tutorials/tutorial-forecasting
https://docs.timescale.com/v0.11/getting-started
https://docs.timescale.com/v1.1/tutorials/tutorial-hello-nyc#tutorial-postgis

posted on 2019-01-21 20:15 荣锋亮阅读(906) 评论(0) 编辑收藏举报

刷新页面返回顶部

rongfengliang-荣锋亮