使用方法 ..... 331 7.4.2 FAQs ..... 332 7.5 TiDB 集群报警规则 ..... 333 7.5.1 TiDB 报警规则 ..... 333 7.5.2 PD 报警规则 ..... 336 7.5.3 TiKV 报警规则 ..... 341 7.5.4 TiFlash 报警规则 ..... 349 7.5.5 TiDB Binlog 报警规则 8.8.3 tidb-server 启动报错 ..... 397 8.8.4 tikv-server 启动报错 ..... 397 8.8.5 pd-server 启动报错 ..... 398 8.8.6 TiDB/TiKV/PD 进程异常退出 ..... 398 8.8.7 TiKV 进程异常重启 ..... 398 8.8.8 TiDB panic ..... 398 399 8.9.2 2. 延迟明显升高 ..... 400 8.9.3 3. TiDB 问题 ..... 400 8.9.4 4. TiKV 问题 ..... 403 8.9.5 5. PD 问题 ..... 406 8.9.6 6. 生态 Tools 问题 ..... 408 8.9.7 7. 常见日志分析 ..... 412 8.10 TiDB 热点问题处理 .....

9.5.2 FAQs ··· 1145 9.6 TiDB Cluster Alert Rules ··· 1146 9.6.1 TiDB alert rules ··· 1148 9.6.2 PD alert rules ··· 1152 9.6.3 TiKV alert rules ··· 1158 9.6.4 TiFlash alert rules ··· 1167 9.6.5 TiDB Write Best Practices ··· 1867 12.5.5 Best Practices for Monitoring TiDB Using Grafana ··· 1879 12.5.6 PD Scheduling Best Practices ··· 1890 12.5.7 Best Practices for TiKV Performance Tuning with Massive Best practices ··· 1938 12.9.3 DDL-related commands ··· 1939 12.9.4 Common questions ··· 1940 12.10 PD Microservices ··· 1940 12.10.1 Usage scenarios ··· 1941 12.10.2 Restrictions ··· 1941 12.10.3 Usage

TiDB-Operator 实现 ## 有状态服务的运维 • 创建集群: PD join • 增加节点 • 升级服务: PD -> TiKV -> TiDB • 下线节点: 加节点 -> 从 PD 下线节点 -> 删除节点 ## Operator 模式实践: TiDB-Operator • 保证 TiDB 不同组件启动和升级顺序(PD->TiKV->TiDB) • 扩展 k8s apiVersion: pingcap.com/v1 kind: TidbCluster metadata: name: demo-cluster spec: pd: size: 1 image: pingcap/pd:latest tidb: size: 2 image: pingcap/tidb:latest tikv: size: TidbSet kube-scheduler tidb-controller-manager tidbcluster-controller pd-controller tikv-controller tidb-controller Pod PD/TiDB/TiKV PVC tidb-scheduler kube-scheduler tidb-scheduler kubelet

Converting to Markdown We’ve added to_markdown() for creating a markdown table (GH11052) In [1]: df = pd.DataFrame({"A": [1, 2, 3], "B": [1, 2, 3]}, index=['a', 'a', 'b']) In [2]: print(df.to_markdown()) | 3 | 3 | 1.3 Experimental new features 1.3.1 Experimental NA scalar to denote missing values A new pd.NA value (singleton) is introduced to represent scalar missing values. Up to now, pandas used several or None for object-dtype data and pd.NaT for datetime-like data. The goal of pd.NA is to provide a “missing” indicator that can be used consistently across data types. pd.NA is currently used by the nullable

soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice of passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame({ ...: "Name": ["Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry", ...: "Bonnell

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice of passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry",

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice of passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry",

soft and hard, installed), make sure you have pytest >= 6.0 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas pandas In [1]: import pandas as pd To load the pandas package and start working with it, import the package. The community agreed alias for pandas is pd, so loading pandas as pd is assumed standard practice of passengers, I know the name (characters), age (integers) and sex (male/female) data. In [2]: df = pd.DataFrame( ...: { ...: "Name": [ ...: "Braund, Mr. Owen Harris", ...: "Allen, Mr. William Henry",

soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas import pandas as pd Object creation See the Data Structure Intro section. Creating a Series by passing a list of values, letting pandas create a default integer index: In [3]: s = pd.Series([1, 3, 5, np Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: In [5]: dates = pd.date_range('20130101', periods=6) In [6]: dates Out[6]: DatetimeIndex(['2013-01-01', '2013-01-02',

row containing a single datetime64 or timedelta64 column (GH31649) • Fixed regression where setting pd.options.display.max_colwidth was not accepting negative inte- ger. In addition, this behavior has been RawIOBase is not recognize encoding option (GH31575) 1.2 Deprecations • Support for negative integer for pd.options.display.max_colwidth is deprecated in favor of using None (GH31532) 1.3 Bug fixes Datetimelike soft and hard, installed), make sure you have pytest >= 5.0.1 and Hypothesis >= 3.58, then run: >>> pd.test() running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site- ˓→packages\pandas