Amazon Timestream 是一种快速、可扩展的无服务器时间序列数据库服务，适用于物联网和运营应用程序，使用该服务每天可以轻松存储和分析数万亿个事件，速度提高了 1000 倍，而成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，并根据用户定义的策略将历史数据移至成本优化的存储层，Amazon Timestream 为客户节省了管理时间序列数据生命周期的时间和成本。Amazon Timestream 专门构建的查询引擎可用于访问和分析近期数据和历史数据，而无需在查询中显式指定数据是保存在内存中还是成本优化层中。Amazon Timestream 内置了时间序列分析函数，可以实现近乎实时地识别数据的趋势和模式。Amazon Timestream 是无服务器服务，可自动缩放以调整容量和性能，因此无需管理底层基础设施，可以专注于构建应用程序。
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、存储和分析，其中包含部署架构、环境部署、数据采集、数据存储和分析，希望当您有类似物联网时序数据存储和分析需求的时候，能从中获得启发，助力业务发展。
架构
Amazon Timestream 能够使用内置的分析函数（如平滑、近似和插值）快速分析物联网应用程序生成的时间序列数据。例如，智能家居设备制造商可以使用 Amazon Timestream 从设备传感器收集运动或温度数据，进行插值以识别没有运动的时间范围，并提醒消费者采取措施（例如减少热量）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分析， 其中架构主要分成三大部分：
实时时序数据采集：通过 Python 数据采集程序结合 Kinesis Stream 和 Kinesis Data Analytics for Apache Flink connector 模拟实现从 PM 2.5 监控设备, 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（成本优化层）存储时长，可以实现近期数据保留在内存中，并根据用户定义的策略将历史数据移至成本优化的存储层。
实时时序数据分析：通过 Grafana （安装 Timesteam For Grafana 插件）实时访问 Timestream 数据，通过 Grafana 丰富的分析图表形式，结合 Amazon Timestream 内置的时间序列分析函数，可以实现近乎实时地识别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 创建 Cloudformation
请使用自己帐号 (region 请选择 us-east-1)
下载 Github 上 Cloudformation Yaml 文件：
git clone https://github.com/bingbingliu18/Timestream-pm25
Timestream-pm25 目录中包含下面 Cloudformation 所用文件 timestream-short-new.yaml
其它都选择缺省， 点击 Create Stack button.
Cloud Formation 创建成功
1.2 连接到新建的 Ec2 堡垒机:
修改证书文件权限
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, 输入： “us-east-1”，其它选择缺省设置。
1.3 连接到 EC2 堡垒机 安装相应软件
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 安装 Grafana Server
连接到 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
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安装 grafana server:
sudo yum install -y grafana
启动 grafana server:
sudo service grafana-server startsudo service grafana-server status
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配置 grafana server 在操作系统启动时 自动启动:
sudo /sbin/chkconfig --add grafana-server
1.6 安装 timestream Plugin
sudo grafana-cli plugins install grafana-timestream-datasource
重启 grafana
sudo service grafana-server restart
1.7 配置 Grafana 要访问 Timesteam 服务所用的 IAM Role
获取 IAM Role Name
选择 IAM 服务， 选择要修改的 role, role name:
timestream-iot-grafanaEC2rolelabview-us-east-1
修改 role trust relationship:
将 Policy document 全部选中， 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" } ]}
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修改后的 trust relationship:
1.8 登录到 Grafana server
第一次登录到 Grafana Server:
1、打开浏览器 访问 http://[Grafana server public ip]:3000
2、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， 访问 Cloudformation output：
3、在登陆界面, 输入 username: admin; password:admin.(输入用户名和密码都是 admin)
4、点击 Log In.登陆成功后， 会收到提示修改密码
1.9 Grafana server 中增加 Timestream 数据源
增加 Timestream 数据源
1.10 Grafana server 中配置 Timestream 数据源
拷贝配置所需要 role ARN 信息 （从 cloudformation output tab）Default Region: us-east-1
IoT 数据存储
2.1 创建 Timestream 数据库 iot
2.2 创建 Timestream 表 pm25
IoT 数据导入
3.1 安装 Flink connector to Timestream
安装 java8
sudo yum install -y java-1.8.0-openjdk*
java -version
安装 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
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change java version from 1.7 to 1.8
sudo update-alternatives --config java
sudo update-alternatives --config javac
安装 Apache Flink
最新的 Apache Flink 版本支持 Kinesis Data Analytics 是 1.8.2.
Create flink folder
cd
mkdir flink
cd flink
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
解压 Apache Flink 源代码:
tar -xvf flink-1.8.2-src.tgz
进入到 Apache Flink 源代码目录:
cd flink-1.8.2
Compile and install Apache Flink (这个编译时间比较长 需要大致 20 分钟):
mvn clean install -Pinclude-kinesis -DskipTests
3.2 创建 Kinesis Data Stream Timestreampm25Stream
aws kinesis create-stream --stream-name Timestreampm25Stream --shard-count 1
3.3 运行 Flink Connector 建立 Kinesis 连接到 Timestream:
cdcd amazon-timestream-tools/integrations/flink_connectormvn clean compile
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数据采集过程中 请持续运行以下命令：
mvn exec:java -Dexec.mainClass="com.amazonaws.services.kinesisanalytics.StreamingJob" -Dexec.args="--InputStreamName Timestreampm25Stream --Region us-east-1 --TimestreamDbName iot --TimestreamTableName pm25"
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3.4 准备 PM2.5 演示数据:
连接到 EC2 堡垒机
下载 5 演示数据生成程序：
cd
mkdir pm25
cd pm25
下载 Github 上数据采集 Python 程序：
git clone https://github.com/bingbingliu18/Timestream-pm25
cd Timestream-pm25
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 请持续运行以下命令：
python3 pm25_new_kinisis_test.py
IoT 数据分析
4.1 登陆到 Grafana Server 创建仪表板和 Panel
创建 Dashboard 查询时 请设定时区为本地浏览器时区:
创建新的 Panel:
选择要访问的数据源， 将要查询分析所执行的 SQL 语句粘贴到新的 Panel 中:
4.2 创建时间数据分析仪表版 Dashboard PM2.5 Analysis 1(Save as PM2.5 Analysis 1)
4.2.1 查询北京各个监控站点 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_pm25FROM "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)
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选择图形显示 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 查询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 chongmingFrom(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25FROM "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)
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Save Panel as Shanghai PM2.5 analysis
Edit Panel Title：Shanghai PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.3 查询广州各个监控站点 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 huadufrom( SELECT location, round(avg(measure_value::bigint),0) as avg_pm25FROM "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)
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Save Panel as Guangzhou PM2.5 analysis
Edit Panel Title：Guangzhou PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.4 查询深圳各个监控站点 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 MeishaFrom(SELECT location, round(avg(measure_value::bigint),0) as avg_pm25FROM "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)
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Save Panel as Shenzhen PM2.5 analysis
Edit Panel Title：Shenzhen PM2.5 analysis
Save Dashboard PM2.5 analysis 1
4.2.5 深圳华侨城时间序列分析(最近 5 分钟内 PM2.5 分析)
New Panel
select location, CREATE_TIME_SERIES(time, measure_value::bigint) as PM25 FROM iot.pm25where measure_name='pm2.5' and location='huaqiao city'and time >= ago(5m)GROUP BY location
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选择图形显示 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 分析
Save Dashboard PM2.5 analysis 1
4.2.6 找出过去 2 小时内深圳华侨城以 30 秒为间隔的平均 PM2.5 值 （使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_PM25FROM interpolated_timeseriesCROSS JOIN UNNEST(interpolated_avg_PM25)
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选择图形显示 select Lines：
Save Panel as Shen Zhen Huaqiao City PM2.5 analysis 1
Edit Panel Title： 过去 2 小时深圳华侨城平均 PM2.5 值 （使用线性插值填充缺失值）
Save Dashboard PM2.5 analysis 1
4.2.7 过去 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 Lasafrom(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_PM25FROM interpolated_timeseriesCROSS JOIN UNNEST(interpolated_avg_PM25))select city,avg(interpolated_avg_PM25) AS inter_avg_PM25from all_location_interpolatedgroup by cityorder by avg(interpolated_avg_PM25) desc)
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选择 Panel 图形类型：
Save Panel as all city analysis 1
Edit Panel Title： 过去 5 分钟所有城市 PM2.5 平均值
Save Dashboard PM2.5 analysis 1
4.2.8 过去 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_PM25FROM interpolated_timeseriesCROSS JOIN UNNEST(interpolated_avg_PM25))select city,location, avg(interpolated_avg_PM25) as avg_PM25_locfrom interpolated_cross_joingroup by city,locationorder by avg_PM25_loc desclimit 10
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选择 Table
Save Panel as all city analysis 2
Edit Panel Title：过去 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 过去 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_PM25FROM interpolated_timeseriesCROSS JOIN UNNEST(interpolated_avg_PM25))select city,location, avg(interpolated_avg_PM25) as avg_PM25_locfrom interpolated_cross_joingroup by city,locationorder by avg_PM25_loc asclimit 10
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选择 Table
Save Panel as all city analysis 3
Edit Panel Title：过去 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、存储和分析，其中包含部署架构、环境部署、数据采集、数据存储和分析，希望当您有类似物联网时序数据存储和分析需求的时候，有所启发，实现海量物联网时序数据高效管理、挖掘物联网数据中蕴含的规律、模式和价值，助力业务发展。
附录
《Amazon Timestream 开发人员指南》
《AWS Timestream 开发程序示例》
《AWS Timestream 与 Grafana 集成示例》
本篇作者
刘冰冰
AWS 数据库解决方案架构师，负责基于 AWS 的数据库解决方案的咨询与架构设计，同时致力于大数据方面的研究和推广。在加入 AWS 之前曾在 Oracle 工作多年，在数据库云规划、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富的经验。
更多信息，请访问链接：https://aws.amazon.com/cn/getting-started/databases/get-started/?nc=sn&loc=4&trk=fab55528-7c2e-4517-b90e-65b760ecfc1c&sc_channel=el
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