zillion 0.3.6

Make sense of it all.

Zillion: Make sense of it all

Introduction

Zillion is a free, open data warehousing and dimensional modeling tool that allows combining and analyzing data from multiple datasources through a simple API. It writes SQL so you don't have to, and it easily bolts onto existing database infrastructure via SQLAlchemy.

With Zillion you can:

• Define a warehouse that contains a variety of SQL and/or file-like datasources
• Define or reflect metrics, dimensions, and relationships in your data
• Run multi-datasource reports and combine the results in a DataFrame
• Flexibly aggregate your data with multi-level rollups and table pivots
• Customize or combine fields with formulas
• Apply technical transformations including rolling, cumulative, and rank statistics
• Apply automatic type conversions - i.e. get a "year" dimension for free from a "date" column
• Save and share report specifications
• Utilize ad hoc or public datasources, tables, and fields to enrich reports

Table of Contents

• Installation
• Primer
  • Metrics and Dimensions
  • Warehouse Theory
  • Query Layers
  • Executing Reports
• Example - Sales Analytics
  • Warehouse Creation
  • Reports
• Advanced Topics
  • FormulaMetrics
  • DataSource Formulas
  • Type Conversions
  • Config Variables
  • DataSource Priority
  • AdHocMetrics
  • AdHocDataTables
  • Technicals
• Supported DataSources
• Docs
• How to Contribute

Installation

⚠️ Warning: This project is in an alpha state and is rapidly changing.

$ pip install zillion

---

Primer

The following is meant to give a quick overview of some theory and nomenclature used in data warehousing with Zillion. Skip below for a quickstart example.

In short: Zillion writes SQL for you and makes data accessible through a very simple API:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "=", "Partner A")
    ]
)

Metrics and Dimensions

In Zillion there are two main types of Fields that will be used in your report requests:

1. Dimensions: attributes of data used for labelling, grouping, and filtering
2. Metrics: facts and measures that may be broken down along dimensions

A Field encapsulates the concept of a column in your data. For example, you may have a Field called "revenue". That Field may occur across several datasources or possibly in multiple tables within a single datasource. Zillion understands that all of those columns represent the same concept, and it can try to use any of them to satisfy reports requesting "revenue".

Likewise there are two main types of tables used to structure your warehouse:

1. Dimension Tables: reference/attribute tables containing only related dimensions
2. Metric Tables: fact tables that may contain metrics and some related dimensions/attributes

Dimension tables are often static or slowly growing in terms of row count and contain attributes tied to a primary key. Some common examples would be lists of US Zip Codes or company/partner directories.

Metric tables are generally more transactional in nature. Some common examples would be records for web requests, ecommerce sales, or stock market price history.

Warehouse Theory

If you really want to go deep on dimensional modeling and the drill-across querying technique Zillion employs, I recommend reading Ralph Kimball's book on data warehousing.

To summarize, drill-across querying forms one or more queries to satisfy a report request for metrics that may exist across multiple datasources and/or tables at a particular dimension grain.

Zillion technically supports snowflake or star schemas, though it isn't picky about it. You can specify table relationships through a parent-child lineage, and Zillion can also infer acceptable joins based on the presence of dimension table primary keys.

Query Layers

Zillion reports can be thought of as running in two layers:

1. DataSource Layer: SQL queries against the warehouse's datasources
2. Combined Layer: A final SQL query against the combined data from the DataSource Layer

The Combined Layer is just another SQL database (in-memory SQLite by default) that is used to tie the datasource data together and apply a few additional features such as rollups, row filters, row limits, sorting, pivots, and technical computations.

---

Executing Reports

The main purpose of Zillion is to execute reports against a Warehouse. You'll see how to initialize a Warehouse in a bit, but at a high level you will be crafting reports as follows:

result = warehouse.execute(
    metrics=["revenue", "leads"],
    dimensions=["date"],
    criteria=[
        ("date", ">", "2020-01-01"),
        ("partner", "=", "Partner A")
    ]
)
print(result.df) # Pandas DataFrame

When comparing to writing SQL, it's helpful to think of the dimensions as the target columns of a group by SQL statement. Think of the metrics as the columns you are aggregating. Think of the criteria as the where clause. Your criteria are applied in the DataSource Layer SQL queries.

The ReportResult has a Pandas DataFrame with the dimensions as the index and the metrics as the columns.

A Report is said to have a grain, which defines the dimensions each metric must be able to join to in order to satisfy the Report requirements. The grain is a combination of all dimensions, including those referenced in criteria or in metric formulas. In the example above, the grain would be {date, partner}. Both "revenue" and "leads" must be able to join to those dimensions for this report to be possible.

These concepts can take time to sink in and obviously vary with the specifics of your data model, but you will become more familiar with them as you start putting together reports against your data warehouses.

---

Example - Sales Analytics

Below we will walk through a simple hypothetical sales data model that demonstrates basic DataSource and Warehouse configuration and then shows some sample reports. The data is a simple SQLite database that is part of the Zillion test code. For reference, the schema is as follows:

CREATE TABLE partners (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL UNIQUE,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE campaigns (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL UNIQUE,
  category VARCHAR NOT NULL,
  partner_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE leads (
  id INTEGER PRIMARY KEY,
  name VARCHAR NOT NULL,
  campaign_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE sales (
  id INTEGER PRIMARY KEY,
  item VARCHAR NOT NULL,
  quantity INTEGER NOT NULL,
  revenue DECIMAL(10, 2),
  lead_id INTEGER NOT NULL,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Warehouse Creation

A Warehouse may be created from an existing SQLAlchemy MetaData instance, purely from a JSON configuration, or a combination of the two. The code below shows how it can be done in as little as one line of code if you have a pointer to a JSON Warehouse config.

Note: Defining your config in JSON is recommended, so we'll save an example of defining Zillion metadata directly on your SQLAlchemy objects for another time.

from zillion import Warehouse

wh = Warehouse(config="https://raw.githubusercontent.com/totalhack/zillion/master/examples/example_wh_config.json")

This example config uses a data_url in its DataSource connect info that tells Zillion to dynamically download that data and connect to it as a SQLite database. This is useful for quick examples or analysis, though in most scenarios you would put a connection string to an existing database like you see here

The basics of Zillion's configuration structure are as follows:

A Warehouse config has the following main sections:

• metrics: optional list of metric configs for global metrics
• dimensions: optional list of dimension configs for global dimensions
• datasources: mapping of datasource names to datasource configs or config URLs

A DataSource config has the following main sections:

• connect: database connection url or dict of connect params
• metrics: optional list of metric configs specific to this datasource
• dimensions: optional list of dimension configs specific to this datasource
• tables: mapping of table names to table configs or config URLs

Tip: datasource and table configs may also be replaced with a URL that points to a local or remote config file.

In this example all four tables in our database are included in the config, two as dimension tables and two as metric tables. The tables are linked through a parent->child relationship: partners to campaigns, and leads to sales. Some tables also utilize the create_fields flag to automatically create Fields on the datasource from column definitions. Other metrics and dimensions are defined explicitly.

To view the structure of this Warehouse after init you can use the print_info method which shows all metrics, dimensions, tables, and columns that are part of your data warehouse:

wh.print_info() # Formatted print of the Warehouse structure

For a deeper dive of the config schema please see the full docs.

Reports

Example: Get sales, leads, and revenue by partner:

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name"]
)

print(result.df)
"""
              sales  leads  revenue
partner_name
Partner A        11      4    165.0
Partner B         2      2     19.0
Partner C         5      1    118.5
"""

Example: Let's limit to Partner A and break down by its campaigns:

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["campaign_name"],
    criteria=[("partner_name", "=", "Partner A")]
)

print(result.df)
"""
               sales  leads  revenue
campaign_name
Campaign 1A        5      2       83
Campaign 2A        6      2       82
"""

Example: The output below shows rollups at the campaign level within each partner, and also a rollup of totals at the partner and campaign level.

Note: the output contains a special character to mark DataFrame rollup rows that were added to the result. The ReportResult object contains some helper attributes to automatically access or filter rollups, as well as a df_display attribute that returns the result with friendlier display values substituted for special characters. The under-the-hood special character is left here for illustration, but may not render the same in all scenarios.

from zillion import RollupTypes

result = wh.execute(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name", "campaign_name"],
    rollup=RollupTypes.ALL
)

print(result.df)
"""
                            sales  leads  revenue
partner_name campaign_name
Partner A    Campaign 1A      5.0    2.0     83.0
             Campaign 2A      6.0    2.0     82.0
             􏿿               11.0    4.0    165.0
Partner B    Campaign 1B      1.0    1.0      6.0
             Campaign 2B      1.0    1.0     13.0
             􏿿                2.0    2.0     19.0
Partner C    Campaign 1C      5.0    1.0    118.5
             􏿿                5.0    1.0    118.5
􏿿            􏿿               18.0    7.0    302.5
"""

See the Report docs for more information on supported rollup behavior.

Example: Save a report spec (not the data):

spec_id = wh.save_report(
    metrics=["sales", "leads", "revenue"],
    dimensions=["partner_name"]
)

Example: Load and run a report from a spec ID:

result = wh.execute_id(spec_id)

Note: The ZILLION_CONFIG environment var can point to a yaml config file. The database used to store Zillion report specs can be configured by setting the ZILLION_DB_URL value in your Zillion config to a valid database connection string. By default a SQLite DB in /tmp is used. See this sample config.

Example: Unsupported Grain

If you attempt an impossible report, you will get an UnsupportedGrainException. The report below is impossible because it attempts to break down the leads metric by a dimension that only exists in a child table. Generally speaking, child tables can join back up to parents to find dimensions, but not the other way around.

# Fails with UnsupportedGrainException
result = wh.execute(
    metrics=["leads"],
    dimensions=["sale_id"]
)

---

Advanced Topics

Formula Metrics

In our example above our config included a formula-based metric called "rpl", which is simply revenue / leads. A FormulaMetric combines other metrics and/or dimensions to calculate a new metric at the Combined Layer of querying. The syntax must match your Combined Layer database, which is SQLite in our example.

{
    "name": "rpl",
    "aggregation": "mean",
    "rounding": 2,
    "formula": "{revenue}/{leads}"
}

DataSource Formulas

Our example also includes a metric "sales" whose value is calculated via formula at the DataSource Layer of querying. Note the following in the fields list for the "id" param in the "main.sales" table. These formulas are in the syntax of the particular DataSource database technology, which also happens to be SQLite in our example.

"fields": [
    "sale_id",
    {"name":"sales", "ds_formula": "COUNT(DISTINCT sales.id)"}
]

Type Conversions

Our example also automatically created a handful of dimensions from the "created_at" columns of the leads and sales tables. Support for type conversions is limited, but for date/datetime columns in supported DataSource technologies you can get a variety of dimensions for free this way.

The output of wh.print_info will show the added dimensions, which are prefixed with "lead_" or "sale_" as specified by the optional type_conversion_prefix in the config for each table. Some examples of auto-generated dimensions include sale_hour, sale_day_name, sale_day_of_month, sale_month, sale_year, etc. To prevent type conversions, set skip_conversion_fields to true on your DataSource config.

See zillion.field.TYPE_ALLOWED_CONVERSIONS for more details on currently supported conversions.

Config Variables

If you'd like to avoid putting sensitive connection information directly in your DataSource configs you can leverage config variables. In your Zillion yaml config you can specify a DATASOURCE_CONTEXTS section as follows:

DATASOURCE_CONTEXTS:
  my_ds_name:
    user: user123
    pass: goodpassword
    host: 127.0.0.1
    schema: reporting

Then when your DataSource config for the datasource named "my_ds_name" is read, it can use this context to populate variables in your connection url:

"datasources": {
    "my_ds_name": {
        "connect": "mysql+pymysql://{user}:{pass}@{host}/{schema}"
        ...
    }
}

DataSource Priority

On Warehouse init you can specify a default priority order for datasources by name. This will come into play when a report could be satisfied by multiple datasources. DataSources earlier in the list will be higher priority. This would be useful if you wanted to favor a set of faster, aggregate tables that are grouped in a DataSource.

wh = Warehouse(config=config, ds_priority=["aggr_ds", "raw_ds", ...])

Ad Hoc Metrics

You may also define metrics "ad hoc" with each report request. Below is an example that creates a revenue-per-lead metric on the fly. These only exist within the scope of the report, and the name can not conflict with any existing fields:

result = wh.execute(
    metrics=[
        "leads",
        {"formula": "{revenue}/{leads}", "name": "my_rpl"}
    ],
    dimensions=["partner_name"]
)

Ad Hoc Tables

Zillion also supports creation or syncing of ad hoc tables in your database during DataSource or Warehouse init. An example of a table config that does this is shown here. It uses the table config's data_url and if_exists params to control the syncing and/or creation of the "main.dma_zip" table from a remote CSV in a SQLite database. The same can be done in other database types too.

The potential performance drawbacks to such an approach should be obvious, particularly if you are initializing your warehouse often or if the remote data file is large. It is often better to sync and create your data ahead of time so you have complete schema control, but this method can be very useful in certain scenarios.

⚠️ Warning: be careful not to overwrite existing tables in your database!

Technicals

There are a variety of technical computations that can be applied to metrics to compute rolling, cumulative, or rank statistics. For example, to compute a 5-point moving average on revenue one might define a new metric as follows:

{
    "name": "revenue_ma_5",
    "type": "numeric(10,2)",
    "aggregation": "sum",
    "rounding": 2,
    "technical": "mean(5)"
}

Technical computations are computed at the Combined Layer, whereas the "aggregation" is done at the DataSource Layer (hence needing to define both above).

For more info on how shorthand technical strings are parsed, see the parse_technical_string code. For a full list of supported technical types see zillion.core.TechnicalTypes.

Technicals also support two modes: "group" and "all". The mode controls how to apply the technical computation across the data's dimensions. In "group" mode, it computes the technical across the last dimension, whereas in "all" mode in computes the technical across all data without any regard for dimensions.

The point of this becomes more clear if you try to do a "cumsum" technical across data broken down by something like ["partner_name", "date"]. If "group" mode is used (the default in most cases) it will do cumulative sums within each partner over the date ranges. If "all" mode is used, it will do a cumulative sum across every data row. You can be explicit about the mode by appending it to the technical string: i.e. "cumsum:all" or "mean(5):group"

---

Supported DataSources

Zillion's goal is to support any database technology that SQLAlchemy supports. That said the support and testing levels in Zillion vary at the moment. In particular, the ability to do type conversions, database reflection, and kill running queries all require some database-specific code for support. The following list summarizes known support levels. Your mileage may vary with untested database technologies that SQLAlchemy supports (it might work just fine, just hasn't been tested yet). Please report bugs and help add more support!

• SQLite: supported and tested
• MySQL: supported and tested
• PostgreSQL: supported and lightly tested
• MSSQL: not tested
• Oracle: not tested
• BigQuery, Redshift, Snowflake, etc: not tested

Note that this is different than the database support for the Combined Layer database. Currently only SQLite is supported there, though it is planned to make this more generic such that any SQLAlchemy supported database could be used.

---

Documentation

More thorough documentation can be found here. You can supplement your knowledge by perusing the tests directory or the API reference.

---

How to Contribute

Adding support and tests for additional database technologies would be a great help. Please See the contributing guide for more information.