DBMS_SCHEDULER 框架异步调用外部shell 脚本，然后由这个shell脚本提交一个Hadoop Map-Reduce 作业。该表函数与映射器 (mapper) 之 间使用 Oracle 高级队列特性进行通信。Hadoop mapper 将数据排入一个公共队列，而表函数则 从该队列中取出数据。由于该表函数能够并行运行，因此使用额外的逻辑来确保仅有一个服 务进程提交外部作业。 3 中存储的数据的一个模板实现。显然可能存在其他的甚至可能更好的实现。 下图是图 2 中原始示意图在技术上更准确、更具体的展示，解释了我们要在何处、如何使用 后文给出的部分实际代码： 图 3. 启动 Mapper 作业并检索数据 第 1 步是确定由谁作为查询协调器。对此我们采用一种将具有相同键值的记录写入表的简单 机制。首个插入胜出，作为此进程的查询协调器 (QC)。请注意，QC 表函数调用同时也承担 bash 脚本。这个 bash 脚本就是图 3 中的启动程 序 (launcher)，它在 Hadoop 集群上启动 mapper 进程（第 3 步）。 5 Oracle 白皮书 — 通过 Oracle 并行处理集成 Hadoop 数据 mapper 进程处理数据，并在第 5 步写入一个队列。在本文的示例中，我们选择了一个在集群 范围内可用的队列。现在，我们

jd.com docker 原理与应用实践 张成远 docker 原理与应用实践 Agenda • 容器系统整体架构 • Namespace • CGroup • Device Mapper • Pull Image • Start Container • Stop Container • Docker Image Storage 容器系统整体架构 Namespace 控制任务访问设备 • freezer— 挂起 / 恢复任务 Device Mapper • DM 框架为上层应用提供了丰富的设备映射及 IO 策 略方面的支持 • Docker 存储端实现之一使用 DM - thin provision • 上层通过 dmsetup 工具或 libdevmapper 库使用 Device Mapper Docker storage driver • Docker • dmsetup message /dev/mapper/pool 0 "create_thin 0" • dmsetup create thin --table "0 2097152 thin /dev/mapper/pool 0" • mkfs.ext4 /dev/mapper/thin • mount /dev/mapper/thin /export Docker storage

centos lvdisplay mkfs.xfs -n ftype=1 /dev/mapper/centos-docker mkdir -p /var/lib/docker mount /dev/mapper/centos-docker /var/lib/docker echo "/dev/mapper/centos-docker /var/lib/docker xfs defaults defaults 0 0" >> /etc/fstab xfs_growfs /dev/mapper/centos-docker df –Th A2. 创建 k8s-node1, k8s-node2, k8s-node3 内存：16GB, 系统盘：40GB，docker 数据盘：40GB，ceph 数据盘：200GB 【注】所有节点(k8s-master, k8s-node1, k8s-node2 Step5. 关闭 swap 分区 swapoff -a && sysctl -w vm.swappiness=0 //设置 swap 开机不启动 vi /etc/fstab #/dev/mapper/centos-swap swap swap defaults 0 0 Step6. 配置 ulimt ulimit -SHn

DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to. In [242]: df.rename({'one': 'foo', 'two': 'bar'}, axis='columns') previous page groupby([by, axis, level, as_index, sort, ...]) Group DataFrame or Series using a mapper or by a Series of columns. gt(other[, level, fill_value, axis]) Greater than of series and other matching indices as other ob- ject. rename([index]) Alter Series index labels or name. rename_axis([mapper, index, columns, axis, . . . ]) Set the name of the axis for the index or columns. reorder_levels(order)

variables (GH23573, GH30959) • Series.map() now accepts collections.abc.Mapping subclasses as a mapper (GH29733) • Added an experimental attrs for storing global metadata about a dataset (GH29062) • 1 >>> df.rename(mapper={0: 1}, index={0: 2}) 0 2 1 pandas 1.0.0 >>> df.rename({0: 1}, index={0: 2}) Traceback (most recent call last): ... TypeError: Cannot specify both 'mapper' and any of 'index' 'index' or 'columns' >>> df.rename(mapper={0: 1}, index={0: 2}) Traceback (most recent call last): ... TypeError: Cannot specify both 'mapper' and any of 'index' or 'columns' You can still change the axis

sparseness conversions); is a view groupby([by, axis, level, as_index, sort, ...]) Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of matching indices to myself. rename([index]) Alter axes input function or functions. rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions. reorder_levels(order) level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of

sparseness conversions); is a view groupby([by, axis, level, as_index, sort, ...]) Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of matching indices to myself. rename([index]) Alter axes input function or functions. rename_axis(mapper[, axis, copy, inplace]) Alter index and / or columns using input function or functions. reorder_levels(order) level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs) Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of

DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to. 200 Chapter 2. User Guide pandas: powerful Python data analysis 314 kind = inspect.Parameter.POSITIONAL_OR_KEYWORD /pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, ˓→inplace, level, errors) (continues on next page) 2.11. Duplicate 4439 4 3 6 4440 """ -> 4441 return super().rename( 4442 mapper=mapper, 4443 index=index, /pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, ˓→inplace, level, errors)

DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to. 232 Chapter 2. User Guide pandas: powerful Python data analysis 326 kind = inspect.Parameter.POSITIONAL_OR_KEYWORD /pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, ˓→inplace, level, errors) (continues on next page) 2.11. Duplicate 5032 4 3 6 5033 """ -> 5034 return super().rename( 5035 mapper=mapper, 5036 index=index, /pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy, ˓→inplace, level, errors)

DataFrame.rename() also supports an “axis-style” calling convention, where you specify a single mapper and the axis to apply that mapping to. In [247]: df.rename({"one": "foo", "two": "bar"}, axis="columns") /pandas/pandas/core/frame.py in rename(self, mapper, index, columns, axis, copy, inplace, ˓→ level, errors) 5037 4 3 6 5038 """ -> 5039 return super().rename( 5040 mapper=mapper, 5041 index=index, /pandas/pandas/core/generic /pandas/pandas/core/generic.py in rename(self, mapper, index, columns, axis, copy,␣ ˓→inplace, level, errors) 1162 return None 1163 else: -> 1164 return result.__finalize__(self, method="rename") 1165 1166