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Amazon Timestream 是一种疾速、可扩大的无服务器工夫序列数据库服务,实用于物联网和经营应用程序,应用该服务每天能够轻松存储和剖析数万亿个事件,速度进步了 1000 倍,而老本仅为关系数据库的十分之一。 通过将近期数据保留在内存中,并依据用户定义的策略将历史数据移至老本优化的存储层,Amazon Timestream 为客户节俭了治理工夫序列数据生命周期的工夫和老本。Amazon Timestream 专门构建的查问引擎可用于拜访和剖析近期数据和历史数据,而无需在查问中显示指定数据是保留在内存中还是老本优化层中。Amazon Timestream 内置了工夫序列剖析函数,能够实现近乎实时地辨认数据的趋势和模式。Amazon Timestream 是无服务器服务,可主动缩放以调整容量和性能,因而无需治理底层基础设施,能够专一于构建应用程序。
本文介绍通过 Timestream、Kinesis Stream 托管服务和 Grafana 和 Flink Connector 开源软件实现物联网(以 PM 2.5 场景为示例)时序数据实时采集、存储和剖析,其中蕴含部署架构、环境部署、数据采集、数据存储和剖析,心愿当您有类似物联网时序数据存储和剖析需要的时候,能从中取得启发,助力业务倒退。
架构
Amazon Timestream 可能应用内置的剖析函数(如平滑、近似和插值)疾速剖析物联网应用程序生成的工夫序列数据。 例如,智能家居设施制造商能够应用 Amazon Timestream 从设施传感器收集静止或温度数据,进行插值以辨认没有静止的工夫范畴,并揭示消费者采取措施(例如缩小热量)以节约能源。
本文物联网(以 PM 2.5 场景为示例),实现 PM2.5 数据实时采集、时序数据存储和实时剖析,其中架构次要分成三大部分:
- 实时时序数据采集:通过 Python 数据采集程序联合 Kinesis Stream 和 Kinesis Data Analytics for Apache Flink connector 模仿实现从 PM 2.5 监控设施, 将数据实时采集数据到 Timestream。
- 时序数据存储:通过 Amazon Timestream 时序数据库实现时序数据存储,设定内存和磁性存储(老本优化层)存储时长,能够实现近期数据保留在内存中,并依据用户定义的策略将历史数据移至老本优化的存储层。
- 实时时序数据分析:通过 Grafana(装置 Timesteam For Grafana 插件)实时拜访 Timestream 数据,通过 Grafana 丰盛的剖析图表模式,联合 Amazon Timestream 内置的工夫序列剖析函数,能够实现近乎实时地辨认物联网数据的趋势和模式。
具体的架构图如下:
部署环境
1.1 创立 Cloudformation
请应用本人帐号 (region 请抉择 us-east-1) 下载 Cloudformation Yaml 文件:
https://bigdata-bingbing.s3-a…
其它都抉择缺省,点击 Create Stack button.
Cloud Formation 创立胜利
1.2 连贯到新建的 Ec2 堡垒机:
批改证书文件权限
chmod 0600 [path to downloaded .pem file]ssh -i [path to downloaded .pem file] ec2-user@[bastionEndpoint]
执行 aws configure:
aws configure
default region name, 输出:“us-east-1”,其它抉择缺省设置。
1.3 连贯到 EC2 堡垒机 装置相应软件
设置时区
TZ='Asia/Shanghai'; export TZ
Install python3
sudo yum install -y python3
Install python3 pip
sudo yum install -y python3-pip
pip3 install boto3
sudo pip3 install boto3
pip3 install numpy
sudo pip3 install numpy
install git
sudo yum install -y git
1.4 下载 Github Timesteram Sample 程序库
git clone https://github.com/awslabs/amazon-timestream-tools amazon-timestream-tools
1.5 装置 Grafana Server
连贯到 EC2 堡垒机:
sudo vi /etc/yum.repos.d/grafana.repo
For OSS releases:(拷贝以下内容到 grafana.repo)
[grafana]
name=grafana
baseurl=https://packages.grafana.com/oss/rpm
repo_gpgcheck=1
enabled=1
gpgcheck=1
gpgkey=https://packages.grafana.com/gpg.key
sslverify=1
sslcacert=/etc/pki/tls/certs/ca-bundle.crt
装置 grafana server:
sudo yum install -y grafana
启动 grafana server:
sudo service grafana-server start
sudo service grafana-server status
配置 grafana server 在操作系统启动时 主动启动:
sudo /sbin/chkconfig --add grafana-server
1.6 装置 timestream Plugin
sudo grafana-cli plugins install grafana-timestream-datasource
重启 grafana
sudo service grafana-server restart
1.7 配置 Grafana 要拜访 Timesteam 服务所用的 IAM Role
获取 IAM Role Name
抉择 IAM 服务,抉择要批改的 role, role name:
timestream-iot-grafanaEC2rolelabview-us-east-1
批改 role trust relationship:
将 Policy document 全副选中,替换成以下内容:
{
"Version": "2012-10-17",
"Statement": [
{"Sid":"","Effect":"Allow","Principal": {"Service":"ec2.amazonaws.com"},"Action":"sts:AssumeRole"
},
{"Sid":"","Effect":"Allow","Principal": {"AWS":"[ 请替换成 CloudFormation output 中的 role arn]"},"Action":"sts:AssumeRole"
}
]
}
批改后的 trust relationship:
1.8 登录到 Grafana server
第一次登录到 Grafana Server:
- 关上浏览器,拜访 http://[Grafana server public ip]:3000
- 缺省的 Grafana Server 监听端口是:3000
如何获取 Ec2 Public IP 地址,如下图所示,拜访 Cloudformation output:
- 在登陆界面, 输出 username: admin; password:admin.( 输出用户名和明码都是 admin)
- 点击 Log In. 登陆胜利后,会收到提醒批改明码
1.9 Grafana server 中减少 Timestream 数据源
减少 Timestream 数据源
1.10 Grafana server 中配置 Timestream 数据源
拷贝配置所须要 role ARN 信息(从 cloudformation output tab)Default Region: us-east-1
IoT 数据存储
2.1 创立 Timestream 数据库 iot
2.2 创立 Timestream 表 pm25
IoT 数据导入
3.1 装置 Flink connector to Timestream
装置 java8
sudo yum install -y java-1.8.0-openjdk*
java -version
装置 debug info, otherwise jmap will throw exception
sudo yum --enablerepo='*-debug*' install -y java-1.8.0-openjdk-debuginfo
Install maven
sudo wget https://repos.fedorapeople.org/repos/dchen/apache-maven/epel-apache-maven.repo -O /etc/yum.repos.d/epel-apache-maven.repo
sudo sed -i s/\$releasever/6/g /etc/yum.repos.d/epel-apache-maven.repo
sudo yum install -y apache-maven
mvn --version
change java version from 1.7 to 1.8
sudo update-alternatives --config java
sudo update-alternatives --config javac
装置 Apache Flink
最新的 Apache Flink 版本反对 Kinesis Data Analytics 是 1.8.2.
1. Create flink folder
cd
mkdir flink
cd flink
2. 下载 Apache Flink version 1.8.2 源代码:
wget https://archive.apache.org/dist/flink/flink-1.8.2/flink-1.8.2-src.tgz
3. 解压 Apache Flink 源代码:
tar -xvf flink-1.8.2-src.tgz
4. 进入到 Apache Flink 源代码目录:
cd flink-1.8.2
5. Compile and install Apache Flink (这个编译工夫比拟长 须要大抵 20 分钟):
mvn clean install -Pinclude-kinesis -DskipTests
3.2 创立 Kinesis Data Stream Timestreampm25Stream
aws kinesis create-stream --stream-name Timestreampm25Stream --shard-count 1
3.3 运行 Flink Connector 建设 Kinesis 连贯到 Timestream:
cd
cd amazon-timestream-tools/integrations/flink_connector
mvn clean compile
数据采集过程中 请继续运行以下命令:
mvn exec:java -Dexec.mainClass="com.amazonaws.services.kinesisanalytics.StreamingJob" -Dexec.args="--InputStreamName
Timestreampm25Stream --Region us-east-1 --TimestreamDbName iot --TimestreamTableName pm25"
3.4 筹备 PM2.5 演示数据:
连贯到 EC2 堡垒机
1. 下载 5 演示数据生成程序:
cd
mkdir pm25
cd pm25
wget https://bigdata-bingbing.s3-ap-northeast-1.amazonaws.com/pm25_new_kinisis_test.py .
2. 运行 5 演示数据生成程序 (python 程序 2 个参数 –region default: us-east-1; –stream default: Timestreampm25Stream)
数据采集过程中 请继续运行以下命令:
python3 pm25_new_kinisis_test.py
IoT 数据分析
4.1 登陆到 Grafana Server 创立仪表板和 Panel
创立 Dashboard 查问时 请设定时区为本地浏览器时区:
创立新的 Panel:
抉择要拜访的数据源,将要查问剖析所执行的 SQL 语句粘贴到新的 Panel 中:
4.2 创立工夫数据分析仪表版 Dashboard PM2.5 Analysis 1(Save as PM2.5 Analysis 1)
4.2.1 查问北京各个监控站点 PM2.5 平均值
New Panel
SELECT CASE WHEN location = 'fengtai_xiaotun' THEN avg_pm25 ELSE NULL END AS fengtai_xiaotou,
CASE WHEN location = 'fengtai_yungang' THEN avg_pm25 ELSE NULL END AS fengtai_yungang,
CASE WHEN location = 'daxing' THEN avg_pm25 ELSE NULL END AS daxing,
CASE WHEN location = 'wanshou' THEN avg_pm25 ELSE NULL END AS wanshou,
CASE WHEN location = 'gucheng' THEN avg_pm25 ELSE NULL END AS gucheng,
CASE WHEN location = 'tiantan' THEN avg_pm25 ELSE NULL END AS tiantan,
CASE WHEN location = 'yanshan' THEN avg_pm25 ELSE NULL END AS yanshan,
CASE WHEN location = 'miyun' THEN avg_pm25 ELSE NULL END AS miyun,
CASE WHEN location = 'changping' THEN avg_pm25 ELSE NULL END AS changping,
CASE WHEN location = 'aoti' THEN avg_pm25 ELSE NULL END AS aoti,
CASE WHEN location = 'mengtougou' THEN avg_pm25 ELSE NULL END AS mentougou,
CASE WHEN location = 'huairou' THEN avg_pm25 ELSE NULL END AS huairou,
CASE WHEN location = 'haidian' THEN avg_pm25 ELSE NULL END AS haidian,
CASE WHEN location = 'nongzhan' THEN avg_pm25 ELSE NULL END AS nongzhan,
CASE WHEN location = 'tongzhou' THEN avg_pm25 ELSE NULL END AS tongzhou,
CASE WHEN location = 'dingling' THEN avg_pm25 ELSE NULL END AS dingling,
CASE WHEN location = 'yanqing' THEN avg_pm25 ELSE NULL END AS yanqing,
CASE WHEN location = 'guanyuan' THEN avg_pm25 ELSE NULL END AS guanyuan,
CASE WHEN location = 'dongsi' THEN avg_pm25 ELSE NULL END AS dongsi,
CASE WHEN location = 'shunyi' THEN avg_pm25 ELSE NULL END AS shunyiFROM
(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25
FROM "iot"."pm25"
where measure_name='pm2.5'
and city='Beijing'
and time >= ago(30s)
group by location,bin(time,30s)
order by avg_pm25 desc)
抉择图形显示 select Gauge
Save Panel as Beijing PM2.5 analysis
Edit Panel Title:Beijing PM2.5 analysis
Save Dashboard PM2.5 analysis 1:
4.2.2 查问上海一天内各个监控站点 PM2.5 平均值
New Panel
SELECT CASE WHEN location = 'songjiang' THEN avg_pm25 ELSE NULL END AS songjiang,
CASE WHEN location = 'fengxian' THEN avg_pm25 ELSE NULL END AS fengxian,
CASE WHEN location = 'no 15 factory' THEN avg_pm25 ELSE NULL END AS No15_factory,
CASE WHEN location = 'xujing' THEN avg_pm25 ELSE NULL END AS xujing,
CASE WHEN location = 'pujiang' THEN avg_pm25 ELSE NULL END AS pujiang,
CASE WHEN location = 'putuo' THEN avg_pm25 ELSE NULL END AS putuo,
CASE WHEN location = 'shangshida' THEN avg_pm25 ELSE NULL END AS shangshida,
CASE WHEN location = 'jingan' THEN avg_pm25 ELSE NULL END AS jingan,
CASE WHEN location = 'xianxia' THEN avg_pm25 ELSE NULL END AS xianxia,
CASE WHEN location = 'hongkou' THEN avg_pm25 ELSE NULL END AS hongkou,
CASE WHEN location = 'jiading' THEN avg_pm25 ELSE NULL END AS jiading,
CASE WHEN location = 'zhangjiang' THEN avg_pm25 ELSE NULL END AS zhangjiang,
CASE WHEN location = 'miaohang' THEN avg_pm25 ELSE NULL END AS miaohang,
CASE WHEN location = 'yangpu' THEN avg_pm25 ELSE NULL END AS yangpu,
CASE WHEN location = 'huinan' THEN avg_pm25 ELSE NULL END AS huinan,
CASE WHEN location = 'chongming' THEN avg_pm25 ELSE NULL END AS chongming
From(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25
FROM "iot"."pm25"
where measure_name='pm2.5'
and city='Shanghai'
and time >= ago(30s)
group by location,bin(time,30s)
order by avg_pm25 desc)
Save Panel as Shanghai PM2.5 analysis
Edit Panel Title:Shanghai PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.3 查问广州各个监控站点 PM2.5 平均值
New Panel
SELECT CASE WHEN location = 'panyu' THEN avg_pm25 ELSE NULL END AS panyu,
CASE WHEN location = 'commercial school' THEN avg_pm25 ELSE NULL END AS commercial_school,
CASE WHEN location = 'No 5 middle school' THEN avg_pm25 ELSE NULL END AS No_5_middle_school,
CASE WHEN location = 'guangzhou monitor station' THEN avg_pm25 ELSE NULL END AS Guangzhou_monitor_station,
CASE WHEN location = 'nansha street' THEN avg_pm25 ELSE NULL END AS Nansha_street,
CASE WHEN location = 'No 86 middle school' THEN avg_pm25 ELSE NULL END AS No_86_middle_school,
CASE WHEN location = 'luhu' THEN avg_pm25 ELSE NULL END AS luhu,
CASE WHEN location = 'nansha' THEN avg_pm25 ELSE NULL END AS nansha,
CASE WHEN location = 'tiyu west' THEN avg_pm25 ELSE NULL END AS tiyu_west,
CASE WHEN location = 'jiulong town' THEN avg_pm25 ELSE NULL END AS jiulong_town,
CASE WHEN location = 'huangpu' THEN avg_pm25 ELSE NULL END AS Huangpu,
CASE WHEN location = 'baiyun' THEN avg_pm25 ELSE NULL END AS Baiyun,
CASE WHEN location = 'maofeng mountain' THEN avg_pm25 ELSE NULL END AS Maofeng_mountain,
CASE WHEN location = 'chong hua' THEN avg_pm25 ELSE NULL END AS Chonghua,
CASE WHEN location = 'huadu' THEN avg_pm25 ELSE NULL END AS huadu
from(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25
FROM "iot"."pm25"
where measure_name='pm2.5'
and city='Guangzhou'
and time >= ago(30s)
group by location,bin(time,30s)
order by avg_pm25 desc)
Save Panel as Guangzhou PM2.5 analysis
Edit Panel Title:Guangzhou PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.4 查问深圳各个监控站点 PM2.5 平均值
New Panel
SELECT CASE WHEN location = 'huaqiao city' THEN avg_pm25 ELSE NULL END AS Huaqiao_city,
CASE WHEN location = 'xixiang' THEN avg_pm25 ELSE NULL END AS xixiang,
CASE WHEN location = 'guanlan' THEN avg_pm25 ELSE NULL END AS guanlan,
CASE WHEN location = 'longgang' THEN avg_pm25 ELSE NULL END AS Longgang,
CASE WHEN location = 'honghu' THEN avg_pm25 ELSE NULL END AS Honghu,
CASE WHEN location = 'pingshan' THEN avg_pm25 ELSE NULL END AS Pingshan,
CASE WHEN location = 'henggang' THEN avg_pm25 ELSE NULL END AS Henggang,
CASE WHEN location = 'minzhi' THEN avg_pm25 ELSE NULL END AS Minzhi,
CASE WHEN location = 'lianhua' THEN avg_pm25 ELSE NULL END AS Lianhua,
CASE WHEN location = 'yantian' THEN avg_pm25 ELSE NULL END AS Yantian,
CASE WHEN location = 'nanou' THEN avg_pm25 ELSE NULL END AS Nanou,
CASE WHEN location = 'meisha' THEN avg_pm25 ELSE NULL END AS Meisha
From(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25
FROM "iot"."pm25"
where measure_name='pm2.5'
and city='Shenzhen'
and time >= ago(30s)
group by location,bin(time,30s)
order by avg_pm25 desc)
Save Panel as Shenzhen PM2.5 analysis
Edit Panel Title:Shenzhen PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.5 深圳华侨城工夫序列剖析 (最近 5 分钟内 PM2.5 剖析)
New Panel
select location, CREATE_TIME_SERIES(time, measure_value::bigint) as PM25 FROM iot.pm25
where measure_name='pm2.5'
and location='huaqiao city'
and time >= ago(5m)
GROUP BY location
抉择图形显示 select Lines; Select Points:
Save Panel as Shen Zhen Huaqiao City PM2.5 analysis
Edit Panel Title:深圳华侨城最近 5 分钟 PM2.5 剖析
Save Dashboard PM2.5 analysis 1
4.2.6 找出过来 2 小时内深圳华侨城以 30 秒为距离的均匀 PM2.5 值(应用线性插值填充缺失的值)
New Panel
WITH binned_timeseries AS (SELECT location, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::bigint), 2) AS avg_PM25
FROM "iot".pm25
WHERE measure_name = 'pm2.5'
AND location='huaqiao city'
AND time > ago(2h)
GROUP BY location, BIN(time, 30s)
), interpolated_timeseries AS (
SELECT location,
INTERPOLATE_LINEAR(CREATE_TIME_SERIES(binned_timestamp, avg_PM25),
SEQUENCE(min(binned_timestamp), max(binned_timestamp), 30s)) AS interpolated_avg_PM25
FROM binned_timeseries
GROUP BY location
)
SELECT time, ROUND(value, 2) AS interpolated_avg_PM25
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_PM25)
抉择图形显示 select Lines:
Save Panel as Shen Zhen Huaqiao City PM2.5 analysis 1
Edit Panel Title:过来 2 小时深圳华侨城均匀 PM2.5 值(应用线性插值填充缺失值)
Save Dashboard PM2.5 analysis 1
4.2.7 过来 5 分钟内所有城市 PM2.5 平均值排名(线性插值)
New Panel
SELECT CASE WHEN city = 'Shanghai' THEN inter_avg_PM25 ELSE NULL END AS Shanghai,
CASE WHEN city = 'Beijing' THEN inter_avg_PM25 ELSE NULL END AS Beijing,
CASE WHEN city = 'Guangzhou' THEN inter_avg_PM25 ELSE NULL END AS Guangzhou,
CASE WHEN city = 'Shenzhen' THEN inter_avg_PM25 ELSE NULL END AS Shenzhen,
CASE WHEN city = 'Hangzhou' THEN inter_avg_PM25 ELSE NULL END AS Hangzhou,
CASE WHEN city = 'Nanjing' THEN inter_avg_PM25 ELSE NULL END AS Nanjing,
CASE WHEN city = 'Chengdu' THEN inter_avg_PM25 ELSE NULL END AS Chengdu,
CASE WHEN city = 'Chongqing' THEN inter_avg_PM25 ELSE NULL END AS Chongqing,
CASE WHEN city = 'Tianjin' THEN inter_avg_PM25 ELSE NULL END AS Tianjin,
CASE WHEN city = 'Shenyang' THEN inter_avg_PM25 ELSE NULL END AS Shenyang,
CASE WHEN city = 'Sanya' THEN inter_avg_PM25 ELSE NULL END AS Sanya,
CASE WHEN city = 'Lasa' THEN inter_avg_PM25 ELSE NULL END AS Lasa
from(
WITH binned_timeseries AS (SELECT city,location, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::bigint), 2) AS avg_PM25
FROM "iot".pm25
WHERE measure_name = 'pm2.5'
AND time > ago(5m)
GROUP BY city,location, BIN(time, 30s)
), interpolated_timeseries AS (
SELECT city,location,
INTERPOLATE_LINEAR(CREATE_TIME_SERIES(binned_timestamp, avg_PM25),
SEQUENCE(min(binned_timestamp), max(binned_timestamp), 30s)) AS interpolated_avg_PM25
FROM binned_timeseries
GROUP BY city,location
), all_location_interpolated as (SELECT city,location,time, ROUND(value, 2) AS interpolated_avg_PM25
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_PM25))
select city,avg(interpolated_avg_PM25) AS inter_avg_PM25
from all_location_interpolated
group by city
order by avg(interpolated_avg_PM25) desc)
抉择 Panel 图形类型:
Save Panel as all city analysis 1
Edit Panel Title:过来 5 分钟所有城市 PM2.5 平均值
Save Dashboard PM2.5 analysis 1
4.2.8 过来 5 分钟内 PM2.5 最高的十个采集点(线性插值)
New Panel
WITH binned_timeseries AS (SELECT city,location, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::bigint), 2) AS avg_PM25
FROM "iot".pm25
WHERE measure_name = 'pm2.5'
AND time > ago(5m)
GROUP BY city,location, BIN(time, 30s)
), interpolated_timeseries AS (
SELECT city,location,
INTERPOLATE_LINEAR(CREATE_TIME_SERIES(binned_timestamp, avg_PM25),
SEQUENCE(min(binned_timestamp), max(binned_timestamp), 30s))
AS interpolated_avg_PM25
FROM binned_timeseries
GROUP BY city,location
), interpolated_cross_join as (SELECT city,location,time, ROUND(value, 2) AS interpolated_avg_PM25
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_PM25))
select city,location, avg(interpolated_avg_PM25) as avg_PM25_loc
from interpolated_cross_join
group by city,location
order by avg_PM25_loc desc
limit 10
抉择 Table
Save Panel as all city analysis 2
Edit Panel Title:过来 5 分钟内 PM2.5 最高的十个采集点(线性插值)
Save Dashboard PM2.5 analysis 1
4.2.9 过来 5 分钟内 PM2.5 最低的十个采集点(线性插值)
New Panel
WITH binned_timeseries AS (SELECT city,location, BIN(time, 30s) AS binned_timestamp, ROUND(AVG(measure_value::bigint), 2) AS avg_PM25
FROM "iot".pm25
WHERE measure_name = 'pm2.5'
AND time > ago(5m)
GROUP BY city,location, BIN(time, 30s)
), interpolated_timeseries AS (
SELECT city,location,
INTERPOLATE_LINEAR(CREATE_TIME_SERIES(binned_timestamp, avg_PM25),
SEQUENCE(min(binned_timestamp), max(binned_timestamp), 30s))
AS interpolated_avg_PM25
FROM binned_timeseries
GROUP BY city,location
), interpolated_cross_join as (SELECT city,location,time, ROUND(value, 2) AS interpolated_avg_PM25
FROM interpolated_timeseries
CROSS JOIN UNNEST(interpolated_avg_PM25))
select city,location, avg(interpolated_avg_PM25) as avg_PM25_loc
from interpolated_cross_join
group by city,location
order by avg_PM25_loc asc
limit 10
抉择 Table
Save Panel as all city analysis 3
Edit Panel Title:过来 5 分钟内 PM2.5 最低的十个采集点(线性插值)
Save Dashboard PM2.5 analysis 1
设置仪表板 每 5 秒钟刷新一次:
本 blog 着重介绍通过 Timestream、Kinesis Stream 托管服务和 Grafana 实现物联网(以 PM 2.5 场景为示例)时序数据实时采集、存储和剖析,其中蕴含部署架构、环境部署、数据采集、数据存储和剖析,心愿当您有类似物联网时序数据存储和剖析需要的时候,有所启发,实现海量物联网时序数据高效治理、开掘物联网数据中蕴含的法则、模式和价值,助力业务倒退。
附录:
《Amazon Timestream 开发人员指南》
https://docs.aws.amazon.com/z…
《Amazon Timestream 开发程序示例》
https://github.com/awslabs/am…
《Amazon Timestream 与 Grafana 集成示例》
https://docs.aws.amazon.com/z…
本篇作者:刘冰冰
亚马逊云科技数据库解决方案架构师,负责基于亚马逊云科技的数据库解决方案的征询与架构设计,同时致力于大数据方面的钻研和推广。在退出亚马逊云科技之前曾在 Oracle 工作多年,在数据库云布局、设计运维调优、DR 解决方案、大数据和数仓以及企业应用等方面有丰盛的教训。