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https://github.com/taogeyt/pyetl
python ETL framework
https://github.com/taogeyt/pyetl
csv data-analytics data-pipeline data-platform db es etl etl-process excel export hive mysql oracle python sql sqlserver
Last synced: 9 days ago
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python ETL framework
- Host: GitHub
- URL: https://github.com/taogeyt/pyetl
- Owner: taogeYT
- License: apache-2.0
- Created: 2017-09-05T10:04:34.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2021-09-08T15:52:02.000Z (over 3 years ago)
- Last Synced: 2024-12-19T09:06:43.708Z (9 days ago)
- Topics: csv, data-analytics, data-pipeline, data-platform, db, es, etl, etl-process, excel, export, hive, mysql, oracle, python, sql, sqlserver
- Language: Python
- Homepage:
- Size: 131 KB
- Stars: 98
- Watchers: 8
- Forks: 36
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pyetl
Pyetl is a **Python 3.6+** ETL framework
## Installation:
```shell script
pip3 install pyetl
```
## Example
```python
import sqlite3
import pymysql
from pyetl import Task, DatabaseReader, DatabaseWriter, ElasticsearchWriter, FileWriter
src = sqlite3.connect("file.db")
reader = DatabaseReader(src, table_name="source_table")
# 数据库之间数据同步，表到表传输
dst = pymysql.connect(host="localhost", user="your_user", password="your_password", db="test")
writer = DatabaseWriter(dst, table_name="target_table")
Task(reader, writer).start()
# 数据库表导出到文件
writer = FileWriter(file_path="./", file_name="file.csv")
Task(reader, writer).start()
# 数据库表同步es
writer = ElasticsearchWriter(index_name="target_index")
Task(reader, writer).start()
```
#### 原始表目标表字段名称不同
```python
import sqlite3
from pyetl import Task, DatabaseReader, DatabaseWriter
con = sqlite3.connect("file.db")
# 原始表source_table包含uuid，full_name字段
reader = DatabaseReader(con, table_name="source_table")
# 目标表target_table包含id，name字段
writer = DatabaseWriter(con, table_name="target_table")
# columns配置目标表和原始表的字段映射
columns = {"id": "uuid", "name": "full_name"}
Task(reader, writer, columns=columns).start()
```
#### 添加字段的udf映射，对字段进行规则校验、数据标准化、数据清洗等
```python
# functions配置字段的udf映射，如下id转字符串，name去除前后空格
functions={"id": str, "name": lambda x: x.strip()}
Task(reader, writer, columns=columns, functions=functions).start()
```
#### 继承Task，灵活扩展
```python
import json
from pyetl import Task, DatabaseReader, DatabaseWriter
class NewTask(Task):
reader = DatabaseReader("sqlite:///db.sqlite3", table_name="source")
writer = DatabaseWriter("sqlite:///db.sqlite3", table_name="target")
def get_columns(self):
"""通过函数的方式生成字段映射配置，使用更灵活"""
# 以下示例将数据库中的字段映射配置取出后转字典类型返回
sql = "select columns from task where name='new_task'"
columns = self.writer.db.read_one(sql)["columns"]
return json.loads(columns)
def get_functions(self):
"""通过函数的方式生成字段的udf映射"""
# 以下示例将每个字段类型都转换为字符串
return {col: str for col in self.columns}
def apply_function(self, record):
"""数据流中对一整条数据的udf"""
record["flag"] = int(record["id"]) % 2
return record
def before(self):
"""任务开始前要执行的操作, 如初始化任务表，创建目标表等"""
sql = "create table destination_table(id int, name varchar(100))"
self.writer.db.execute(sql)
def after(self):
"""任务完成后要执行的操作，如更新任务状态等"""
sql = "update task set status='done' where name='new_task'"
self.writer.db.execute(sql)
NewTask().start()
```
## Reader和Writer
| Reader | 介绍 |
| ------------------- | -------------------------- |
| DatabaseReader | 支持所有关系型数据库的读取 |
| FileReader | 结构化文本数据读取，如csv文件 |
| ExcelReader | Excel表文件读取 |
| ElasticsearchReader | 读取es索引数据 |
| Writer | 介绍 |
| ------------------- | -------------------------- |
| DatabaseWriter | 支持所有关系型数据库的写入 |
| ElasticsearchWriter | 批量写入数据到es索引 |
| HiveWriter | 批量插入hive表 |
| HiveWriter2 | Load data方式导入hive表（推荐) |
| FileWriter | 写入数据到文本文件 |