| Name | petaly JSON |
| Version |
0.0.8.post2
JSON |
| download |
| home_page | None |
| Summary | Python ETL tool |
| upload_time | 2024-12-13 12:53:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
petaly
etl
ingestion
database
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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|
## Welcome to Petaly!
Petaly is an open-source ETL (Extract, Transform, Load) tool created by and for data professionals!
Our mission is to simplify data movement and transformation across different platforms with a tool that truly understands the needs of the data community.
Designed for seamless data exchange, Petaly supports PostgreSQL, MySQL and CSV formats, with plans to expand and integrate additional open-source technologies.
It makes connecting and transferring data across various systems effortless. Petaly is user-friendly and requires no programming knowledge.
Data pipelines can be easily configured using the YAML format, making the tool ready to use immediately after installation.
## Important
This is an Alpha version of the Petaly project!
## Getting Started
Explore the documentation below:
<br>**[1. Installation](#petaly-install)**
<br>**[2. Init config file and workspace](#petaly-init-config-workspace)**
<br>**[3. Init a pipeline](#petaly-init-pipeline)**
<br>**[4. Run the pipeline](#petaly-run-pipeline)**
<br>**[5. Load CSV to Postgres](#petaly-load-csv-postgres-examples)** (Step-by-Step Tutorial)
<br>**[6. Pipeline better explained](#petaly-pipeline-explained)**
<br>**[7. More Examples](#petaly-pipeline-more-examples)**
## Tool Features
In the current version Petaly provides extract and load data between following endpoints:
- CSV
- MySQL (tested version 8.0+)
- PostgreSQL (tested version 16+)
## Requirements:
- Python 3.10 - 3.12
## Tested on
Petaly was tested on:
OS:
- MacOS 14.6
- Ubuntu 22.04.3 LTS
It's possible that the tool will work with other operating systems and other databases and python versions. It just hasn't been tested yet.
<a id="petaly-install"></a>
## 1. Installation
Petaly can be installed using `pip` or downloaded directly from this repository.
### Install with pip
```
$ mkdir petaly
$ cd petaly
$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip3 install petaly
```
### Alternatively, download and install from GitHub
```
$ git clone https://github.com/petaly-labs/petaly.git
$ cd petaly
$ python3 -m venv .venv
$ source .venv/bin/activate
$ pip3 install -r requirements.txt
$ cd src/
# Installation step completed
```
Check petaly installation.
If petaly was installed with:
`$ pip install petaly`
run:
`$ python3 -m petaly --help`
If the repo was cloned from the GitHub [petaly-labs](https://github.com/petaly-labs/petaly) repository navigate to the src folder first:
`$ cd petaly/scr` and execute the command above
<a id="petaly-init-config-workspace"></a>
## 2. Initialize config file and workspace
This tutorial explains the `petaly.ini` configuration file and how to create a workspace.
### Init config file
To create petaly.ini file run following step once:
`$ python3 -m petaly init -c /absolute-path-to-your-config-dir/petaly.ini`
After the configuration file has been created, either use it always with the [-c] argument
`$ python -m petaly init -c /absolute-path-to-your-config-dir/petaly.ini`
or to skip the [-c] argument, set the environment variable `PETALY_CONFIG_DIR`
`$ export PETALY_CONFIG_DIR=/absolute-path-to-your-config-dir`
### Init workspace
This step must be performed only once after petaly installation.
If the workspace is already initialised, you can skip the following step and start configuring pipelines. If the workspace hasn't been initialised yet, first define following three parameters in petaly.ini
```
pipeline_dir_path=/absolute-path-to-pipelines-dir
logs_dir_path=/absolute-path-to-logs-dir
output_dir_path=/absolute-path-to-output-dir
```
And then initialize the workspace with following command. This command will simply create all these 3 paths defined above in the petaly.ini file.
`$ python3 -m petaly init --workspace -c /path_to_config_dir/petaly.ini`
<a id="petaly-init-pipeline"></a>
## 3. Init a pipeline
Run the following command and follow the wizard steps to initialize a pipeline my_pipeline.
No changes will be made to the target endpoint at this point.
`$ python3 -m petaly init -p my_pipeline -c /path_to_config_dir/petaly.ini`
Run the following command to configure your pipeline. Once the pipeline is created, you can modify it manually.
For detailed instructions, check in the section: **[Pipeline explained](#petaly-pipeline-explained)**
<a id="petaly-run-pipeline"></a>
## 4. Run Pipeline
Now you can run the pipeline my_pipeline and load data from the specified source.
Note that it will make changes, re/create tables in the target endpoint (database or folders)
`$ python3 -m petaly run -p my_pipeline -c /path_to_config_dir/petaly.ini`
<a id="petaly-load-csv-postgres-examples"></a>
## 5. Load CSV file to Postgres
In this tutorial, we’ll show you how to run a new pipeline and load a test CSV file into PostgreSQL.
### 5.1. Before You Start
- Ensure that Petaly is installed properly and your workspace is initialized.
- Refer to our [installation](#petaly-install) and [workspace setup guides](#petaly-init-config-workspace) if needed.
- Install a PostgreSQL server locally, or use a Docker image for setup.
### 5.2. Configure the Pipeline:
- Run the following command to configure your pipeline. For detailed instructions, check the section below **[Pipeline explained](#petaly-pipeline-explained)**
- `$ python3 -m petaly init -p csv_to_postgres -c /path_to_config_dir/petaly.ini`
### 5.3. Download the test files:
- Download the stocks and options CSV files from the repository. [./tests/data/csv/*](./tests/data/csv/) and store it under `/your-directory-path-to-csv-files`
- unzip it under linux or mac with: `gunzip options.csv.gz`, `gunzip stocks.csv.gz`
### 5.4. Set Up the Pipeline:
- Use `csv` as the source and `postgres` as the target. Follow the configuration wizard.
- Specify objects for `stocks` and `options`, and provide the appropriate paths in object_source_dir and file_names inside.
### 5.5. Run the Pipeline:
- Execute the pipeline using the configured settings:
- `$ python3 -m petaly run -p csv_to_postgres -c /path_to_config_dir/petaly.ini`
### 5.6 CSV to Postgres Full Examples
Use the wizard to create the appropriate pipeline skeleton, as different connector types require specific parameters.
Once the pipeline is created, you can modify it using editors like `vi`, `nano`, or any other text editor.
The following examples are provided for reference only. For detailed instructions, and other examples check the section below **[Pipeline explained](#petaly-pipeline-explained)**
#### CSV to Postgres
```
pipeline:
pipeline_attributes:
pipeline_name: csv_to_psql
is_enabled: true
source_attributes:
connector_type: csv
target_attributes:
connector_type: postgres
database_user: root
database_password: db-password
database_host: localhost
database_port: 5432
database_name: petalydb
database_schema: petaly_tutorial
data_attributes:
use_data_objects_spec: only
object_default_settings:
header: true
columns_delimiter: ","
quote_char: none-quote
---
data_objects_spec:
- object_spec:
object_name: stocks
destination_object_name:
recreate_destination_object: true
cleanup_linebreak_in_fields: false
exclude_columns:
-
object_source_dir: /your-directory-path-to-csv-files
file_names:
- stocks.csv
- object_spec:
object_name: options
destination_object_name:
recreate_destination_object: true
cleanup_linebreak_in_fields: false
exclude_columns:
-
object_source_dir: /your-directory-path-to-csv-files
file_names:
- options.csv
```
<a id="petaly-pipeline-explained"></a>
## 6. Pipeline explained
This tutorial provides a step-by-step guide for getting started with Petaly after installation. It begins with an explanation of the petaly.ini configuration file and then dives into the details of the pipeline.yaml file.
### Pipeline configuration
To avoid mistakes during configuration, it is recommended to use the pipeline wizard. The configuration differs depending on the endpoints.
To use pipeline wizard run following command:
`$ python3 -m petaly init -p my_pipeline -c /path_to_config_dir/petaly.ini`
### Pipeline structure
After creating the pipeline, let's review the structure and each parameter.
Here is an example of an export table from mysql to postgres
The pipeline.yaml file contains two documents `pipeline:` and `data_objects_spec:`.
The `pipeline:` document defines `pipeline_attributes:`, `source_attributes:`, `target_attributes:` and `data_attributes:`
The second document in the pipeline.yaml file, separated by three dashes, is the `data_objects_spec:` document which contains the definition of each object/table.
Here is the skeleton of the whole pipeline.yaml document:
```
pipeline:
pipeline_attributes:
....
source_attributes:
....
target_attributes:
....
data_attributes:
....
---
data_objects_spec:[]
```
### Pipeline Main Blocks:
The structure of a pipeline definition is explained in the following sections.
#### pipeline_attributes
```
pipeline_attributes:
# The name of the pipeline must be unique
pipeline_name: mysql_to_psql
# True indicates that the pipeline is enabled. The default is true
is_enabled: true
```
#### source_attributes
The source attributes specify the source connections. The connection parameters may differ depending on the endpoint type.
```
source_attributes:
# Specify endpoint type: mysql, postgres, csv
connector_type: mysql
# Specify database user name
database_user: root
# Specify database user password in plain text
database_password: dbpassword
# Specify the database hostname or IP address. It can also be remote, if access is given to the machine running petaly, or if an ssh tunnel is provided.
database_host: localhost
# Specify database port
database_port: 3306
# Specify database name
database_name: tutorial_db
```
#### target_attributes
The target attributes specify the target connections. The connection parameters may differ depending on the endpoint type.
```
target_attributes:
# Specify endpoint type: mysql, postgres, csv
connector_type: postgres
# Specify database user name
database_user: postgres
# Specify database user password in plain text
database_password: dbpassword
# Specify the database hostname or IP address. It can also be remote, if access is given to the machine running petaly, or if an ssh tunnel is provided.
database_host: localhost
# Specify database port
database_port: 5432
# Specify database name
database_name: petaly_db
# Specify database schema name
database_schema: petaly
```
#### data_attributes
Following parameters configuring default behavior for data-objects/tables
```
data_attributes:
# provide fine definition: only, prefer, ignore
data_objects_spec_mode: only
```
In this step, you will define the main behaviour of the object definition, as follows:<br>
If **only** [default]: Load only the objects explicitly specified in data_objects_spec[] section. These objects will be configured in the next step.<br>
If **ignore**: Load all objects from the database_schema (or database_name if no schema exists) as defined in the source_attributes section, completely disregarding data_objects_spec[] section.<br>
If **prefer**: Load all objects from the database_schema, but for objects specified in data_objects_spec[], apply the refined configuration defined in that section.<br>
**object_default_settings**
The object_default_settings parameter defines the default configuration options applied to objects during processing.
These settings serve as a baseline and can be overridden by more specific configurations if needed.
This section is used for loading data from CSV files, as well as for extracting and loading data from databases.
It provides a standardized configuration to facilitate seamless data exchange between different engines, such as MySQL and PostgreSQL.
```
# define csv parse options. It also valid for mysql, postgres extract or load
object_default_settings:
# Specifies whether the file should contains or contains a header line with the names of each column in the file.
header: true
# The character delimiting individual cells in the CSV data.
columns_delimiter: ","
# Choose between double-quote, single-quote or none-quote. The default is double-quote.
quote_char: double-quote
```
### data_objects_spec
The second document `data_objects_spec:` in the pipeline.yaml file, separated by three dashes, contains none, one or more object (table).
```
---
data_objects_spec:[]
```
As explained above data_objects_spec is only used when it set to **only** or **prefer**. The default mode is **only**
`data_objects_spec_mode: only`
Take a look at the `data_objects_spec:` bellow:
```
---
data_objects_spec:
- object_spec:
object_name: orders
destination_object_name:
...
- object_spec:
object_name: customers
destination_object_name: customers_reloaded
...
```
The `object_name` has to be unique.
This can be a source/destination table name.
Multiple objects can be specified starting with a dash `- object_spec:` and followed by `object_name: table_name` and few others parameters.
Optionally, the destination object/table name can be different. To achieve this specify the parameter `destination_object_name`.
```
---
data_objects_spec:
- object_spec:
object_name: stocks
destination_object_name: stocks_new
```
If the `recreate_destination_object` parameter is true, the target object (table) will be recreated.
Otherwise, the object/table will only be created if it does not exist. The default is false.
```
recreate_destination_object: false
```
Use the `exclude_columns` parameter to exclude specific columns, or leave it blank to include all columns for specific table/object.
If you want to exclude columns: either specify a comma-separated list of columns to exclude in parentheses [] or use dashes '-', one per line. As shown below:
To include all
```
exclude_columns:
- null
```
To exclude column1, column2
```
exclude_columns:
- column1
- column2
```
Alternative approach to exclude column1, column2
```
exclude_columns: [column1, column2]
```
### csv files as source
In `object_source_dir:`, specify the path to the directory where the csv files are stored. This is only relevant for file uploads.
`object_source_dir: /absolute-path-to-file-dir`
Specify the filenames to load specific file or leave blank/null to include all files from `object_source_dir:`.
At least one dash or empty brackets [] should be present.
If you leave it empty/null, remember that all files in `object_source_dir:` should have the same metadata structure as they will be loaded into the same table.
In case you want to load only a specific file/s from the given `object_source_dir:` use dash character one per line and filename.
Use one of the following options:
1. To load all files from the `object_source_dir` set dash to null `- null` or keep dash `-` empty:
```
file_names:
- null
```
Alternatively, to load all files from the `object_source_dir` set:
```
file_names: []
```
2. To load specific files from the `object_source_dir` set:
```
file_names:
- file_name.csv
- file_name_2.csv
- file_name_3.csv
```
Alternatively, to load specific files from `object_source_dir` set:
```
file_names: [file_name.csv, file_name_2.csv, file_name_3.csv]
```
### csv as target
The target file format is specified through connector_type: csv.
The initial data is loaded into the output folder defined in the petaly.ini file.
The destination_file_dir defines the final directory after download the data in the output folder first.
```
pipeline:
....
target_attributes:
connector_type: csv
destination_file_dir: /your-path-to-destination-folder
```
<a id="petaly-pipeline-more-examples"></a>
## 7. More Examples
#### MySQL to Postgres
The following example exports a table `stocks` from Mysql into PostgreSQL under the name `stocks_in_postgres`
```
pipeline:
pipeline_attributes:
pipeline_name: mysql2psql
is_enabled: true
source_attributes:
connector_type: mysql
database_user: root
database_password: db-password
database_host: localhost
database_port: 3306
database_name: petaly_tutorial
target_attributes:
connector_type: postgres
database_user: postgres
database_password: db-password
database_host: localhost
database_port: 5432
database_name: petalydb
database_schema: petaly_tutorial
data_attributes:
data_objects_spec_mode: only
object_default_settings:
header: true
columns_delimiter: ','
quote_char: double-quote
---
data_objects_spec:
- object_spec:
object_name: stocks
destination_object_name: stocks_in_postgres
recreate_destination_object: false
cleanup_linebreak_in_fields: false
exclude_columns:
-
```
#### CSV to MySQL
The following example create a new table and load csv file stocks.csv into Mysql database.
```
pipeline:
pipeline_attributes:
pipeline_name: csv2mysql
is_enabled: true
source_attributes:
connector_type: csv
target_attributes:
connector_type: mysql
database_user: root
database_password: db-password
database_host: localhost
database_port: 3306
database_name: petaly_tutorial
data_attributes:
use_data_objects_spec: only
object_default_settings:
header: true
columns_delimiter: ","
quote_char: none
---
data_objects_spec:
- object_spec:
object_name: stocks
destination_object_name:
recreate_destination_object: true
cleanup_linebreak_in_fields: false
exclude_columns:
-
object_source_dir: /your-path-to-csv-folder/stocks
file_names:
-
- object_spec:
object_name: options
destination_object_name:
recreate_destination_object: true
cleanup_linebreak_in_fields: false
exclude_columns:
-
object_source_dir: /your-directory-path-to-csv-files
file_names:
- options.csv
- options2.csv
```
#### Postgres to CSV
The following example exports tables **stocks*** and ***users** from Postgres into destination folder `destination_file_dir: /your-path-to-destination-folder`
It also exclude columns ***likebroadway***, ***likemusicals*** of table **users** from export.
```
pipeline:
pipeline_attributes:
pipeline_name: psql2csv
is_enabled: true
source_attributes:
connector_type: postgres
database_user: postgres
database_password: db-password
database_host: localhost
database_port: 5432
database_name: petalydb
database_schema: petaly_schema
target_attributes:
connector_type: csv
destination_file_dir: /your-path-to-destination-folder
data_attributes:
data_objects_spec_mode: only
object_default_settings:
header: true
columns_delimiter: ","
quote_char: double-quote
---
data_objects_spec:
- object_spec:
object_name: stocks
destination_object_name: stocks_as_csv
recreate_destination_object: true
cleanup_linebreak_in_fields: true
exclude_columns:
-
- object_spec:
object_name: users
destination_object_name: users_as_csv
recreate_destination_object: true
cleanup_linebreak_in_fields: true
exclude_columns:
- likebroadway
- likemusicals
```
## Let's Build Together 🌱
Join us in building something meaningful together.
The foundation of any open-source project is its community, a group of individuals collaborating, sharing knowledge and contributing to a shared vision. At Petaly, every contribution, no matter the size, plays an important role in shaping the project.
We’re continuously improving Petaly, and your feedback and contributions are invaluable.
Check out our [Contributing Guide](./CONTRIBUTING.md) to see how you can get involved. Connect with fellow contributors, share your experiences and get support in our community channels.
Together, we can make Petaly even better!
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"author": null,
"author_email": "Pavel Rabaev <contact@petaly.org>",
"download_url": "https://files.pythonhosted.org/packages/7d/72/5d6968f6f2e73f73291282a4c4d9cd38b5e330873299b113253e51974696/petaly-0.0.8.post2.tar.gz",
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"description": "\n\n\n\n## Welcome to Petaly!\n\nPetaly is an open-source ETL (Extract, Transform, Load) tool created by and for data professionals! \nOur mission is to simplify data movement and transformation across different platforms with a tool that truly understands the needs of the data community.\n\nDesigned for seamless data exchange, Petaly supports PostgreSQL, MySQL and CSV formats, with plans to expand and integrate additional open-source technologies. \nIt makes connecting and transferring data across various systems effortless. Petaly is user-friendly and requires no programming knowledge. \nData pipelines can be easily configured using the YAML format, making the tool ready to use immediately after installation.\n\n## Important\nThis is an Alpha version of the Petaly project!\n\n## Getting Started\n\nExplore the documentation below:\n \n<br>**[1. Installation](#petaly-install)**\n<br>**[2. Init config file and workspace](#petaly-init-config-workspace)**\n<br>**[3. Init a pipeline](#petaly-init-pipeline)**\n<br>**[4. Run the pipeline](#petaly-run-pipeline)**\n<br>**[5. Load CSV to Postgres](#petaly-load-csv-postgres-examples)** (Step-by-Step Tutorial)\n<br>**[6. Pipeline better explained](#petaly-pipeline-explained)**\n<br>**[7. More Examples](#petaly-pipeline-more-examples)**\n\n\n## Tool Features\nIn the current version Petaly provides extract and load data between following endpoints:\n\n- CSV\n- MySQL (tested version 8.0+)\n- PostgreSQL (tested version 16+)\n\n## Requirements:\n- Python 3.10 - 3.12\n\n## Tested on\nPetaly was tested on: \n\nOS: \n- MacOS 14.6\n- Ubuntu 22.04.3 LTS\n\nIt's possible that the tool will work with other operating systems and other databases and python versions. It just hasn't been tested yet.\n\n<a id=\"petaly-install\"></a>\n\n## 1. Installation\n\nPetaly can be installed using `pip` or downloaded directly from this repository.\n\n### Install with pip\n```\n$ mkdir petaly\n$ cd petaly\n$ python3 -m venv .venv\n$ source .venv/bin/activate\n$ pip3 install petaly\n\n```\n### Alternatively, download and install from GitHub\n\n```\n$ git clone https://github.com/petaly-labs/petaly.git\n$ cd petaly\n$ python3 -m venv .venv\n$ source .venv/bin/activate\n$ pip3 install -r requirements.txt\n$ cd src/\n\n# Installation step completed\n```\n\nCheck petaly installation.\n\nIf petaly was installed with: \n\n`$ pip install petaly`\n\nrun:\n\n`$ python3 -m petaly --help` \n\nIf the repo was cloned from the GitHub [petaly-labs](https://github.com/petaly-labs/petaly) repository navigate to the src folder first:\n\n`$ cd petaly/scr` and execute the command above\n\n<a id=\"petaly-init-config-workspace\"></a>\n\n## 2. Initialize config file and workspace\n\n\nThis tutorial explains the `petaly.ini` configuration file and how to create a workspace.\n\n### Init config file\n\nTo create petaly.ini file run following step once:\n\n`$ python3 -m petaly init -c /absolute-path-to-your-config-dir/petaly.ini`\n\nAfter the configuration file has been created, either use it always with the [-c] argument\n\n`$ python -m petaly init -c /absolute-path-to-your-config-dir/petaly.ini`\n\nor to skip the [-c] argument, set the environment variable `PETALY_CONFIG_DIR`\n\n`$ export PETALY_CONFIG_DIR=/absolute-path-to-your-config-dir`\n\n### Init workspace\n\nThis step must be performed only once after petaly installation. \n\nIf the workspace is already initialised, you can skip the following step and start configuring pipelines. If the workspace hasn't been initialised yet, first define following three parameters in petaly.ini\n\n```\npipeline_dir_path=/absolute-path-to-pipelines-dir\nlogs_dir_path=/absolute-path-to-logs-dir\noutput_dir_path=/absolute-path-to-output-dir\n```\nAnd then initialize the workspace with following command. This command will simply create all these 3 paths defined above in the petaly.ini file.\n\n`$ python3 -m petaly init --workspace -c /path_to_config_dir/petaly.ini` \n\n<a id=\"petaly-init-pipeline\"></a>\n\n## 3. Init a pipeline\n\n\nRun the following command and follow the wizard steps to initialize a pipeline my_pipeline.\nNo changes will be made to the target endpoint at this point.\n\n`$ python3 -m petaly init -p my_pipeline -c /path_to_config_dir/petaly.ini`\n\nRun the following command to configure your pipeline. Once the pipeline is created, you can modify it manually.\n\nFor detailed instructions, check in the section: **[Pipeline explained](#petaly-pipeline-explained)**\n\n<a id=\"petaly-run-pipeline\"></a>\n\n## 4. Run Pipeline\n\nNow you can run the pipeline my_pipeline and load data from the specified source. \nNote that it will make changes, re/create tables in the target endpoint (database or folders)\n\n`$ python3 -m petaly run -p my_pipeline -c /path_to_config_dir/petaly.ini`\n\n<a id=\"petaly-load-csv-postgres-examples\"></a>\n\n## 5. Load CSV file to Postgres\n\nIn this tutorial, we\u2019ll show you how to run a new pipeline and load a test CSV file into PostgreSQL.\n\n### 5.1. Before You Start\n\n- Ensure that Petaly is installed properly and your workspace is initialized. \n- Refer to our [installation](#petaly-install) and [workspace setup guides](#petaly-init-config-workspace) if needed.\n\n- Install a PostgreSQL server locally, or use a Docker image for setup.\n\n### 5.2. Configure the Pipeline:\n\n- Run the following command to configure your pipeline. For detailed instructions, check the section below **[Pipeline explained](#petaly-pipeline-explained)**\n\n- `$ python3 -m petaly init -p csv_to_postgres -c /path_to_config_dir/petaly.ini`\n\n### 5.3. Download the test files:\n\n- Download the stocks and options CSV files from the repository. [./tests/data/csv/*](./tests/data/csv/) and store it under `/your-directory-path-to-csv-files`\n- unzip it under linux or mac with: `gunzip options.csv.gz`, `gunzip stocks.csv.gz` \n\n### 5.4. Set Up the Pipeline:\n\n- Use `csv` as the source and `postgres` as the target. Follow the configuration wizard.\n\n- Specify objects for `stocks` and `options`, and provide the appropriate paths in object_source_dir and file_names inside.\n\n### 5.5. Run the Pipeline:\n\n- Execute the pipeline using the configured settings:\n\n- `$ python3 -m petaly run -p csv_to_postgres -c /path_to_config_dir/petaly.ini`\n\n\n### 5.6 CSV to Postgres Full Examples\n\nUse the wizard to create the appropriate pipeline skeleton, as different connector types require specific parameters.\n\nOnce the pipeline is created, you can modify it using editors like `vi`, `nano`, or any other text editor.\n\nThe following examples are provided for reference only. For detailed instructions, and other examples check the section below **[Pipeline explained](#petaly-pipeline-explained)**\n\n#### CSV to Postgres\n```\npipeline:\n pipeline_attributes:\n pipeline_name: csv_to_psql\n is_enabled: true\n source_attributes:\n connector_type: csv\n target_attributes:\n connector_type: postgres\n database_user: root\n database_password: db-password\n database_host: localhost\n database_port: 5432\n database_name: petalydb\n database_schema: petaly_tutorial\n data_attributes:\n use_data_objects_spec: only\n object_default_settings:\n header: true\n columns_delimiter: \",\"\n quote_char: none-quote\n---\ndata_objects_spec:\n- object_spec:\n object_name: stocks\n destination_object_name:\n recreate_destination_object: true\n cleanup_linebreak_in_fields: false\n exclude_columns:\n -\n object_source_dir: /your-directory-path-to-csv-files\n file_names:\n - stocks.csv\n- object_spec:\n object_name: options\n destination_object_name:\n recreate_destination_object: true\n cleanup_linebreak_in_fields: false\n exclude_columns:\n -\n object_source_dir: /your-directory-path-to-csv-files\n file_names:\n - options.csv\n```\n\n<a id=\"petaly-pipeline-explained\"></a>\n\n## 6. Pipeline explained\n\nThis tutorial provides a step-by-step guide for getting started with Petaly after installation. It begins with an explanation of the petaly.ini configuration file and then dives into the details of the pipeline.yaml file.\n\n### Pipeline configuration\n\nTo avoid mistakes during configuration, it is recommended to use the pipeline wizard. The configuration differs depending on the endpoints.\n\nTo use pipeline wizard run following command:\n\n`$ python3 -m petaly init -p my_pipeline -c /path_to_config_dir/petaly.ini`\n\n### Pipeline structure\nAfter creating the pipeline, let's review the structure and each parameter.\n\nHere is an example of an export table from mysql to postgres\n\nThe pipeline.yaml file contains two documents `pipeline:` and `data_objects_spec:`.\n\nThe `pipeline:` document defines `pipeline_attributes:`, `source_attributes:`, `target_attributes:` and `data_attributes:`\n\nThe second document in the pipeline.yaml file, separated by three dashes, is the `data_objects_spec:` document which contains the definition of each object/table.\n\nHere is the skeleton of the whole pipeline.yaml document:\n\n```\npipeline:\n pipeline_attributes:\n ....\n source_attributes:\n ....\n target_attributes:\n ....\n data_attributes:\n ....\n--- \ndata_objects_spec:[] \n```\n\n### Pipeline Main Blocks:\n\nThe structure of a pipeline definition is explained in the following sections.\n\n#### pipeline_attributes\n\n```\n pipeline_attributes:\n # The name of the pipeline must be unique\n pipeline_name: mysql_to_psql\n \n # True indicates that the pipeline is enabled. The default is true\n is_enabled: true\n \n```\n\n#### source_attributes\n\nThe source attributes specify the source connections. The connection parameters may differ depending on the endpoint type.\n\n```\n source_attributes:\n # Specify endpoint type: mysql, postgres, csv\n connector_type: mysql \n \n # Specify database user name\n database_user: root\n \n # Specify database user password in plain text\n database_password: dbpassword\n \n # Specify the database hostname or IP address. It can also be remote, if access is given to the machine running petaly, or if an ssh tunnel is provided.\n database_host: localhost\n \n # Specify database port\n database_port: 3306\n \n # Specify database name\n database_name: tutorial_db\n \n``` \n#### target_attributes\n\nThe target attributes specify the target connections. The connection parameters may differ depending on the endpoint type.\n\n```\n target_attributes: \n \n # Specify endpoint type: mysql, postgres, csv\n connector_type: postgres\n \n # Specify database user name\n database_user: postgres\n \n # Specify database user password in plain text\n database_password: dbpassword\n \n # Specify the database hostname or IP address. It can also be remote, if access is given to the machine running petaly, or if an ssh tunnel is provided.\n database_host: localhost\n \n # Specify database port\n database_port: 5432\n \n # Specify database name\n database_name: petaly_db\n \n # Specify database schema name\n database_schema: petaly\n```\n#### data_attributes\nFollowing parameters configuring default behavior for data-objects/tables \n```\n data_attributes:\n \n # provide fine definition: only, prefer, ignore \n data_objects_spec_mode: only\n```\nIn this step, you will define the main behaviour of the object definition, as follows:<br>\nIf **only** [default]: Load only the objects explicitly specified in data_objects_spec[] section. These objects will be configured in the next step.<br>\nIf **ignore**: Load all objects from the database_schema (or database_name if no schema exists) as defined in the source_attributes section, completely disregarding data_objects_spec[] section.<br>\nIf **prefer**: Load all objects from the database_schema, but for objects specified in data_objects_spec[], apply the refined configuration defined in that section.<br>\n\n**object_default_settings**\n\nThe object_default_settings parameter defines the default configuration options applied to objects during processing.\nThese settings serve as a baseline and can be overridden by more specific configurations if needed.\n\nThis section is used for loading data from CSV files, as well as for extracting and loading data from databases.\n\nIt provides a standardized configuration to facilitate seamless data exchange between different engines, such as MySQL and PostgreSQL.\n\n\n```\n # define csv parse options. It also valid for mysql, postgres extract or load\n object_default_settings:\n \n # Specifies whether the file should contains or contains a header line with the names of each column in the file.\n header: true\n \n # The character delimiting individual cells in the CSV data.\n columns_delimiter: \",\"\n \n # Choose between double-quote, single-quote or none-quote. The default is double-quote.\n quote_char: double-quote\n \n```\n\n### data_objects_spec\n\nThe second document `data_objects_spec:` in the pipeline.yaml file, separated by three dashes, contains none, one or more object (table). \n\n```\n---\ndata_objects_spec:[]\n```\n\nAs explained above data_objects_spec is only used when it set to **only** or **prefer**. The default mode is **only**\n`data_objects_spec_mode: only`\n\nTake a look at the `data_objects_spec:` bellow:\n\n```\n---\ndata_objects_spec:\n- object_spec:\n object_name: orders\n destination_object_name: \n ...\n- object_spec:\n object_name: customers\n destination_object_name: customers_reloaded\n ... \n```\n\nThe `object_name` has to be unique.\nThis can be a source/destination table name.\nMultiple objects can be specified starting with a dash `- object_spec:` and followed by `object_name: table_name` and few others parameters.\n\nOptionally, the destination object/table name can be different. To achieve this specify the parameter `destination_object_name`.\n\n```\n---\ndata_objects_spec:\n- object_spec:\n object_name: stocks\n destination_object_name: stocks_new\n```\n\nIf the `recreate_destination_object` parameter is true, the target object (table) will be recreated. \nOtherwise, the object/table will only be created if it does not exist. The default is false.\n\n```\n recreate_destination_object: false\n```\n\nUse the `exclude_columns` parameter to exclude specific columns, or leave it blank to include all columns for specific table/object. \nIf you want to exclude columns: either specify a comma-separated list of columns to exclude in parentheses [] or use dashes '-', one per line. As shown below: \n\nTo include all\n``` \n exclude_columns:\n - null\n```\n\nTo exclude column1, column2\n\n```\n exclude_columns:\n - column1\n - column2\n```\n\nAlternative approach to exclude column1, column2\n\n```\n exclude_columns: [column1, column2]\n```\n\n### csv files as source\n\nIn `object_source_dir:`, specify the path to the directory where the csv files are stored. This is only relevant for file uploads.\n\n`object_source_dir: /absolute-path-to-file-dir`\n\nSpecify the filenames to load specific file or leave blank/null to include all files from `object_source_dir:`.\nAt least one dash or empty brackets [] should be present.\nIf you leave it empty/null, remember that all files in `object_source_dir:` should have the same metadata structure as they will be loaded into the same table. \nIn case you want to load only a specific file/s from the given `object_source_dir:` use dash character one per line and filename.\n\nUse one of the following options: \n1. To load all files from the `object_source_dir` set dash to null `- null` or keep dash `-` empty: \n```\n file_names:\n - null\n```\nAlternatively, to load all files from the `object_source_dir` set: \n```\n file_names: []\n```\n2. To load specific files from the `object_source_dir` set: \n```\n\n file_names:\n - file_name.csv\n - file_name_2.csv\n - file_name_3.csv\n```\nAlternatively, to load specific files from `object_source_dir` set:\n\n```\n file_names: [file_name.csv, file_name_2.csv, file_name_3.csv]\n```\n\n### csv as target\n\nThe target file format is specified through connector_type: csv.\n\nThe initial data is loaded into the output folder defined in the petaly.ini file. \nThe destination_file_dir defines the final directory after download the data in the output folder first.\n\n\n```\npipeline:\n ....\n target_attributes:\n connector_type: csv\n destination_file_dir: /your-path-to-destination-folder\n```\n\n<a id=\"petaly-pipeline-more-examples\"></a>\n\n## 7. More Examples\n\n#### MySQL to Postgres\n\nThe following example exports a table `stocks` from Mysql into PostgreSQL under the name `stocks_in_postgres`\n\n```\npipeline:\n pipeline_attributes:\n pipeline_name: mysql2psql\n is_enabled: true\n source_attributes:\n connector_type: mysql\n database_user: root\n database_password: db-password\n database_host: localhost\n database_port: 3306\n database_name: petaly_tutorial\n target_attributes:\n connector_type: postgres\n database_user: postgres\n database_password: db-password\n database_host: localhost\n database_port: 5432\n database_name: petalydb\n database_schema: petaly_tutorial\n data_attributes:\n data_objects_spec_mode: only\n object_default_settings:\n header: true\n columns_delimiter: ','\n quote_char: double-quote\n---\ndata_objects_spec:\n- object_spec:\n object_name: stocks\n destination_object_name: stocks_in_postgres\n recreate_destination_object: false\n cleanup_linebreak_in_fields: false\n exclude_columns:\n -\n```\n\n#### CSV to MySQL\n\nThe following example create a new table and load csv file stocks.csv into Mysql database.\n\n```\npipeline:\n pipeline_attributes:\n pipeline_name: csv2mysql\n is_enabled: true\n source_attributes:\n connector_type: csv\n target_attributes:\n connector_type: mysql\n database_user: root\n database_password: db-password\n database_host: localhost\n database_port: 3306\n database_name: petaly_tutorial\n data_attributes:\n use_data_objects_spec: only\n object_default_settings:\n header: true\n columns_delimiter: \",\"\n quote_char: none\n---\ndata_objects_spec:\n- object_spec:\n object_name: stocks\n destination_object_name:\n recreate_destination_object: true\n cleanup_linebreak_in_fields: false\n exclude_columns:\n -\n object_source_dir: /your-path-to-csv-folder/stocks\n file_names:\n - \n- object_spec:\n object_name: options\n destination_object_name:\n recreate_destination_object: true\n cleanup_linebreak_in_fields: false\n exclude_columns:\n -\n object_source_dir: /your-directory-path-to-csv-files\n file_names:\n - options.csv\n - options2.csv \n```\n\n\n#### Postgres to CSV\n\nThe following example exports tables **stocks*** and ***users** from Postgres into destination folder `destination_file_dir: /your-path-to-destination-folder`\nIt also exclude columns ***likebroadway***, ***likemusicals*** of table **users** from export.\n\n```\npipeline:\n pipeline_attributes:\n pipeline_name: psql2csv\n is_enabled: true\n source_attributes:\n connector_type: postgres\n database_user: postgres\n database_password: db-password\n database_host: localhost\n database_port: 5432\n database_name: petalydb\n database_schema: petaly_schema\n target_attributes:\n connector_type: csv\n destination_file_dir: /your-path-to-destination-folder\n data_attributes:\n data_objects_spec_mode: only\n object_default_settings:\n header: true\n columns_delimiter: \",\"\n quote_char: double-quote\n---\ndata_objects_spec:\n- object_spec:\n object_name: stocks\n destination_object_name: stocks_as_csv\n recreate_destination_object: true\n cleanup_linebreak_in_fields: true\n exclude_columns:\n - \n- object_spec:\n object_name: users\n destination_object_name: users_as_csv\n recreate_destination_object: true\n cleanup_linebreak_in_fields: true\n exclude_columns:\n - likebroadway\n - likemusicals\n```\n\n\n## Let's Build Together \ud83c\udf31\n\nJoin us in building something meaningful together. \nThe foundation of any open-source project is its community, a group of individuals collaborating, sharing knowledge and contributing to a shared vision. At Petaly, every contribution, no matter the size, plays an important role in shaping the project.\nWe\u2019re continuously improving Petaly, and your feedback and contributions are invaluable. \nCheck out our [Contributing Guide](./CONTRIBUTING.md) to see how you can get involved. Connect with fellow contributors, share your experiences and get support in our community channels. \n\nTogether, we can make Petaly even better!\n",
"bugtrack_url": null,
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We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2024, Pavel Rabaev Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
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