Glide: Easy ETL¶

Glide aims to be a simple, reusable, extensible approach to building ETL data pipelines. It provides a suite of nodes and pipelines out of the box that cover many common use cases, such as reading and writing data to/from SQL, URLs, local/remote files, and email.

Glide aims to have sane defaults and rely on standard, well-known Python libraries for data processing under the hood. It strives for familiar behavior out of the provided nodes while allowing for customization by passing arguments through to the underlying libraries in many cases. It’s also very easy to write completely custom nodes and pipelines.

Glide also tries to give you a lot for free, including simple parallel processing support, a variety of ways to manage node/pipeline context, and automatic CLI generation.

Glide encourages extensions that provide nodes and pipelines for interacting with various data sources and data processing modules. Check out the glide.extensions module to see some currently supported extensions, such as Dask for scalable analytics workflows.

Glide: Easy ETL¶

Introduction¶

Glide is an easy-to-use data pipelining tool inspired by Consecution and Apache Storm Topologies.

Like those libraries, Glide is:

A simple, reusable approach to building robust ETL pipelines
A system for wiring together processing nodes to form a directed acyclic graph (DAG)

Glide also has:

An expanding suite of built-in nodes and pipelines that extract, transform, and load data from/to any combination of:
- SQL databases (SQLite, DBAPI, and SQLAlchemy support)
- Local or remote files (CSVs, Excel, and raw file support)
- URLs (JSON endpoints, file downloads, APIs, etc.)
- HTML Tables
- Emails
Extensions for Pandas, Dask, Celery, Redis Queue and more
A variety of node and DAG parallel/concurrent/distributed processing strategies
A simple decorator to generate a command line interface from a pipeline in ~one line of code
Flexible pipeline templating

Glide is not a task orchestration and/or dependency management tool like Airflow. Use Glide to define your easily developed/contained/reusable/testable data processing pipelines and then rely on a tool like Airflow to do what it’s good at, namely scheduling and complex task dependency management.

Table of Contents¶

Installation¶

⚠️ Warning: This project is in an alpha state and is maintained on an as-needed basis. Please test carefully for production usage and report any issues.

$ pip install glide

Primer¶

You are encouraged to take a deeper look at the docs, but the short of it is the following:

1. A Node is a part of a pipeline which has a run method that typically accepts data from upstream nodes, and pushes data to downstream nodes. For example:

class MyNode(Node):
    def run(self, data):
        # Some node-specific code here
        self.push(data)

2. A Glider is a pipeline of Node objects wired together in a DAG. It accepts input data in its consume method. For example:

glider = Glider(
    MyExtractNode("extract")
    | MyTransformNode("transform")
    | MyLoadNode("load")
)
glider.consume(data)

If a node’s run method has additional parameters, they are populated from the node’s context. More info on creating nodes and populating runtime context can be found here.

Basic Examples¶

The following examples serve to quickly illustrate some core features and built-in nodes. There is much more Glide can do that is not shown here. Everything below assumes you have used the following shortcut to import all necessary node and pipeline classes:

from glide import *

Example: CSV Extract, Transform, and Load¶

Apply a transformation to data from a CSV, use a function to lowercase all strings, and load into an output CSV:

def lower_rows(data):
    for row in data:
        for k, v in row.items():
            row[k] = v.lower() if type(v) == str else v
    return data

glider = Glider(
    CSVExtract("extract")
    | Func("transform", func=lower_rows)
    | CSVLoad("load")
)
glider.consume(
    ["/path/to/infile.csv"],
    extract=dict(chunksize=100),
    load=dict(outfile="/path/to/outfile.csv"),
)

Example: SQL Extract and Load¶

Read from one table, write to another:

conn = get_my_sqlalchemy_conn()
sql = "select * from in_table limit 10"

glider = Glider(
    SQLExtract("extract")
    | SQLLoad("load"),
    global_state=dict(conn=conn) # conn is automagically passed to any nodes that accept a "conn" argument
)
glider.consume(
    [sql],
    load=dict(table="out_table")
)

Example: SQL Transactions¶

Start a transaction before writing to a database, rollback on failure:

glider = Glider(
    SQLExtract("extract")
    | SQLTransaction("tx")
    | SQLLoad("load", rollback=True),
    global_state=dict(conn=conn)
)
glider.consume(...)

Example: URL Extraction¶

Extract data from each URL in the list of requests and load to a URL endpoint:

glider = Glider(URLExtract("extract") | URLLoad("load"))
reqs = [
    "https://jsonplaceholder.typicode.com/todos/1",
    "https://jsonplaceholder.typicode.com/todos/2",
]
glider.consume(
    reqs,
    extract=dict(data_type="json"),
    load=dict(
        url="https://jsonplaceholder.typicode.com/todos",
        data_param="json",
        headers={"Content-type": "application/json; charset=UTF-8"},
    ),
)

Flow Control Examples¶

Example: Filters¶

Filter the propagation of data based on the result of a function:

def data_check(node, data):
    # do some check on data, return True/False to control filtering
    return True

glider = Glider(
    MyExtract("extract")
    | Filter("filter", func=data_check)
    | MyLoad("load")
)

Example: IterPush¶

Push each row of an input iterable individually:

glider = Glider(
    CSVExtract("extract", nrows=20)
    | IterPush("iter")
    | Print("load")
)

Example: SplitPush¶

Split an iterable before pushing:

glider = Glider(SplitPush("push", split_count=2) | Print("print"))
glider.consume([range(4)])

Example: SplitByNode¶

Split an iterable evenly among downstream nodes:

glider = Glider(SplitByNode("push") | [Print("print1"), Print("print2")])
glider.consume([range(4)])

Example: Reduce¶

Collect all upstream node data before pushing:

glider = Glider(
    CSVExtract("extract")
    | Reduce("reduce")
    | Print("load")
)
glider.consume(["/path/to/infile1.csv", "/path/to/infile2.csv"])

This will read both input CSVs and push them in a single iterable to the downstream nodes. You can also use the flatten option of Reduce to flatten the depth of the iterable before pushing (effectively a concat operation).

Example: Join¶

Join data on one or more columns before pushing:

glider = Glider(
    Reduce("reduce")
    | Join("join")
    | Print("load")
)
d1 = <list of dicts or DataFrame>
d2 = <list of dicts or DataFrame>
glider.consume([d1, d2], join=dict(on="common_key", how="inner"))

Example: Routers¶

Route data to a particular downstream node using a router function:

def parity_router(row):
    if int(row["mycolumn"]) % 2 == 0:
        return "even"
    return "odd"

glider = Glider(
    CSVExtract("extract", nrows=20)
    | IterPush("iter")
    | [parity_zip_router, Print("even"), Print("odd")]
)
glider.consume(...)

This will push rows with even mycolumn values to the “even” Print node, and rows with odd mycolumn values to the “odd” Print node.

Example: Window Processing¶

Push a sliding window of the data:

glider = Glider(
    CSVExtract("extract", nrows=5)
    | WindowPush("window", size=3)
    | MyWindowCalcNode("calc")
)

Example: Date Windows¶

Generate a set of datetime windows and push them downstream:

import datetime

today = datetime.date.today()
glider = Glider(DateTimeWindowPush("windows") | PrettyPrint("print"))
glider.consume(
    windows=dict(
        start_date=today - datetime.timedelta(days=3),
        end_date=today,
        num_windows=2
    )
)

Or use DateWindowPush for date objects. Note that the data arg to consume can be ignored because the top node (DateTimeWindowPush) is a subclass of NoInputNode which takes no input data and generates data to push on its own.

Example: Return Values¶

By default consume does not return any values and assumes you will be outputting your results to one or more endpoints in your terminating nodes (files, databases, etc.). The Return node will collect the data from its parent node(s) and set it as a return value for consume.

glider = Glider(
    CSVExtract("extract")
    | MyTransformer("transform")
    | Return("return")
)
data = glider.consume(...)

Parallelization & Concurrency¶

Example: Parallel Transformation¶

Call a function in parallel processes on equal splits of data from a CSV:

glider = Glider(
    CSVExtract("extract")
    | ProcessPoolSubmit("transform", push_type=PushTypes.Result)
    | CSVLoad("load")
)
glider.consume(
    ["infile.csv"],
    transform=dict(func=lower_rows),
    load=dict(outfile="outfile.csv"),
)

We passed push_type=PushTypes.Result to force ProcessPoolSubmit to fetch and combine the asynchronous results before pushing to the downstream node. The default is to just pass the asynchronous task/futures objects forward, so the following would be equivalent:

glider = Glider(
    CSVExtract("extract")
    | ProcessPoolSubmit("transform")
    | FuturesReduce("reduce")
    | Flatten("flatten")
    | CSVLoad("load")
)

The FuturesReduce node waits for the results from each futures object, and then Flatten will combine each subresult back together into a single result to be loaded in the final CSVLoad node.

Example: Parallel Pipelines via ParaGlider¶

Completely parallelize a pipeline using a ParaGlider (who said ETL isn’t fun?!?). Split processing of the inputs (two files in this case) over the pool, with each process running the entire pipeline on part of the consumed data:

glider = ProcessPoolParaGlider(
    CSVExtract('extract')
    | Print('load')
)
glider.consume(
    ["/path/to/infile1.csv", "/path/to/infile2.csv"],
    extract=dict(nrows=50)
)

Example: Parallel Branching¶

Branch into parallel execution in the middle of the DAG utilizing a parallel push node:

glider = Glider(
    CSVExtract("extract", nrows=60)
    | ProcessPoolPush("push", split=True)
    | [Print("load1"), Print("load2"), Print("load3")]
)
glider.consume(["/path/to/infile.csv"])

The above example will extract 60 rows from a CSV and then push equal slices to the logging nodes in parallel processes. Using split=False (default) would have passed the entire 60 rows to each logging node in parallel processes.

Once you branch off into processes with a parallel push node there is no way to reduce/join the pipeline back into the original process and resume single-process operation. The entire remainder of the pipeline is executed in each subprocess. However, that is possible with threads as shown in the next example.

Example: Thread Reducers¶

glider = Glider(
    CSVExtract("extract", nrows=60)
    | ThreadPoolPush("push", split=True)
    | [Print("load1"), Print("load2"), Print("load3")]
    | ThreadReduce("reduce")
    | Print("loadall")
)
glider.consume(["/path/to/infile.csv"])

The above code will split the data and push to the first 3 logging nodes in multiple threads. The ThreadReduce node won’t push until all of the previous nodes have finished, and then the final logging node will print all of the results.

Example: Asyncio¶

Limited, experimental support is also available for concurrency via asyncio in Python >= 3.7:

import asyncio

async def async_sleep(data):
    # Dummy example. Await some real async work in here.
    await asyncio.sleep(0.5)
    return data

glider = Glider(
    CSVExtract("extract", nrows=5)
    | AsyncIOSubmit("transform", push_type=PushTypes.Result)
    | Print("load")
)
glider.consume(
    ["/path/to/infile.csv"],
    transform=dict(func=async_sleep)
)

The above example will split the input data into items to be processed on an asyncio event loop and synchronously wait for the results before pushing. AsyncIOSubmit supports specifying a split_count as well as a timeout when waiting for results. Alternatively, one can push asyncio futures and later reduce their results as follows:

glider = Glider(
      CSVExtract("extract", nrows=5)
      | AsyncIOSubmit("transform", push_type=PushTypes.Async)
      | AsyncIOFuturesReduce("reduce", flatten=True)
      | Print("load")
)

Note that the asyncio nodes will create and start an event loop for you if necessary. It’s also perfectly fine to manage the event loop on your own, in which case glide will run tasks on the current thread’s event loop.

Utility Examples¶

Example: Templated Nodes and Pipelines¶

Drop replacement nodes into an existing pipeline. Any node can be replaced by name:

glider = Glider(
    PlaceholderNode("extract")
    | CSVLoad("load")
)
glider["extract"] = CSVExtract("extract")
glider.consume(...)

Or reuse an existing structure of nodes with a NodeTemplate:

nodes = NodeTemplate(
    CSVExtract("extract")
    | CSVLoad("load")
)
glider = Glider(nodes()) # Copy of nodes created with each call

Or reuse an existing pipeline structure with GliderTemplate:

template = GliderTemplate(
    CSVExtract("extract")
    | CSVLoad("load")
)
glider = template() # Copy of pipeline created with each call

Example: Data Integrity Checks¶

You can use the AssertFunc node to assert that some condition of the data is met:

glider = Glider(
    CSVExtract("extract", chunksize=10, nrows=20)
    | AssertFunc("length_check", func=lambda node, data: len(data) == 10)
    | CSVLoad("load")
)

The func callable must accept two parameters, a reference to the node object and the data passed into that node. Any truthy value returned will pass the assertion test.

Similarly, you can do a sql-based check with AssertSQL, in this case simply verifying the number of rows inserted:

glider = Glider(
    SQLExtract("extract")
    | SQLLoad("load")
    | AssertSQL("sql_check")
)

sql = "select * from in_table limit 10"
assert_sql = "select (select count(*) as x from out_table) == 10 as assert"

glider.consume(
    [sql],
    extract=dict(conn=in_conn),
    load=dict(conn=out_conn, table="out_table"),
    sql_check=dict(conn=out_conn, sql=assert_sql)
)

This looks for a truthy value in the assert column of the result to pass the assertion. You can also use the data_check option of AssertSQL to instead have it do a comparison to the result of some function of the data:

glider = ...

sql = "select * from in_table limit 10"
assert_sql = "select count(*) as assert from out_table"

glider.consume(
    [sql],
    extract=dict(conn=in_conn),
    load=dict(conn=out_conn, table="out_table", push_data=True),
    sql_check=dict(
        conn=out_conn,
        sql=assert_sql,
        data_check=lambda node, data: len(data)
    )
)

Note that we also added push_data=True to the SQLLoad node to have it push the data instead of a table name.

Example: Debugging¶

To enable debug logging for Glide change the log level of the “glide” logger:

import logging
logging.getLogger("glide").setLevel(logging.DEBUG)

Glide will then print debug information about data passed through your pipeline.

You can also pass _log=True to the init method of any node to enable logging of processed data:

glider = Glider(
    SQLExtract("extract", _log=True)
    ...
)

Additionaly, you can pass _debug=True to the init method of any node to cause the node to drop into PDB right before calling run, assuming you aren’t executing the pipeline in a subprocess:

glider = Glider(
    SQLExtract("extract", _debug=True)
    ...
)

Finally, there are a variety of print nodes you can place in your pipeline for general logging or debugging, such as Print, PrettyPrint, LenPrint, ReprPrint, and FormatPrint. See the node documentation for more info.

Example: Profiling Pipelines¶

Insert a Profile node somewhere in your pipeline to get profiler information for all downstream nodes:

glider = Glider(
    Profile("profile")
    ...
)

Example: Complex Pipelines¶

If the hierarchy of nodes you want to form is not achievable with the | operator, you can use the add_downstream Node method to form more complex graphs. More info can be found here.

Example: Plotting Pipeline DAGs¶

If you have the Graphviz package installed, you can generate a plot of your pipelines by simply doing the following:

glider = Glider(...)
glider.plot("/path/to/filename.png")

CLI Generation¶

With Glide you can create parameterized command line scripts from any pipeline with a simple decorator:

glider = Glider(
    SQLLoad("extract")
    | SQLExtract("load")
)

@glider.cli()
def main(glide_data, node_contexts):
    glider.consume(glide_data, **node_contexts)

if __name__ == "__main__":
    main()

The script arguments, their types, and whether they are required or not is all inferred by inspecting the run arguments on the nodes of the pipeline and prefixing the node name. Please see the full documentation here for more details.

Extensions¶

To install all extensions and dev dependencies:

$ pip install glide[complete]

You can also just install Glide plus a specific extension:

$ pip install glide[dask]
$ pip install glide[celery]
$ pip install glide[rq]
$ pip install glide[swifter]

To access installed extensions import from the glide.extensions submodules as necessary. Review the documentation and tests for current extensions for help getting started.

Pandas¶

The Pandas extension is actually supported by default with all glide installs. Below is a simple example that extracts from a CSV, lowercases all strings, and then loads to another CSV using Pandas under the hood:

def lower(s):
    return s.lower() if type(s) == str else s

glider = Glider(
    DataFrameCSVExtract("extract")
    | DataFrameApplyMap("transform", func=lower)
    | DataFrameCSVLoad("load", index=False, mode="a")
)
glider.consume(...)

There are a variety of other helpful nodes built in, including ToDataFrame, FromDataFrame, nodes to read/write other datasources, and nodes to deal with rolling calculations. There is also a generic DataFrameMethod node that passes through to any DataFrame method.