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v6.0.0 (latest)
Overview
Installation and Configuration
Loading and Exporting Data
Heavy Immerse
Data Science
UDF and UDTF
Tutorials and Demos
Troubleshooting and Special Topics
Introduction to Ibis
Explore large datasets in HEAVY.AI with the full power of SQL, with a pandas-like API
Ibis allows you to explore data in a variety of remote storage systems, with an API is inspired by the pandas toolkit.
You can use Ibis to interact directly with HeavyDB and several other supported SQL systems by writing high-level Python code instead of lower-level SQL. Using familiar tools can increase productivity.
Ibis support for several execution backends including common SQL databases and processing engines. This allows you to author analytical workflows or operations once and run them consistently against these backends. You can also create complex workflows that can simultaneously materialize data from multiple backends and combine the resulting outputs in various ways.
Supported backends include PySpark, Google BigQuery, and PostgreSQL.
Install the backend ibis-heavyai
You can install ibis-heavyai using
conda (recommended) or mamba, which is a faster implementation of conda. For more information, see the Mamba documentation.1
conda install -c conda-forge ibis-heavyai
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​
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# or to install rbc to a new environemnt, run
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conda create -n ibis -c conda-forge ibis-heavyai
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conda activate ibis
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​
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# check if ibis-heavyai installed succesfully
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python -c 'import ibis; ibis.heavyai.version'
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You can also use
pip for package management:1
pip install ibis-heavyai
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# check if ibis-heavyai installed succesfully
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python -c 'import ibis; ibis.heavyai.version'
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Ibis and HeavyDB
This short example demonstrates Ibis. Inside a notebook cell, you can first connect to an HeavyDB database instance:
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import ibis
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​
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con = ibis.heavyai.connect(
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host='localhost',
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database='heavyai',
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user='admin',
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password='HyperInteractive'
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)
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If you are launching JupyterLab from Immerse, this connection is already set up for you based on your Immerse login and credentials.
This connection object has several utility methods that can come in handy, for listing database objects, creating tables, loading data etc.
Next, let's identify a table and define a simple Ibis expression against it:
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tablename = 'github'
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t = con.table(tablename)
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count_expr = t.count()
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count_expr.compile()
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This expression is compiled by Ibis into SQL that runs against HeavyDB:
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SELECT count(*) AS "count"
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FROM github
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When executed, the above SQL, as expected, counts the rows of the table:
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print(f'Count of records in table {tablename} is {count_expr.execute()}')
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Count of records in table github is 2033049065
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You can also write raw SQL in Ibis. A SQL query can be wrapped as an Ibis expression and composed together with other Ibis expressions.
Ibis offers a powerful way to compose sophisticated analytical expressions, and then leverage the processing power and scale of supported storage backends to evaluate them and return results. The following shows a more complex Ibis expression and the SQL it generates. You can leverage a full programming language (Python) to assemble complex expressions that cover SQL features like joins, filters, and aggregates.
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well_list = ['4201334812', '4201335379', '4201335112', '4201334947', '4201334329', '4201334965', '4201334978', '4201334998', '4201335467', '4201334917', '4201334930', '4212331826', '4212331164', '4212300017', '4212331782', '4212332076', '4212331839', '4228532777', '4228533451', '4212332354', '4212332207', '4212330405', '4212332014']
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​
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#Find oil well production for the specific well list above
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sum_oil=t.oil_monthly.sum().log10().name('oil_monthly')
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expr = (t.filter([t.api_no_10.isin(well_list), t.oil_monthly.notnull()])\
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.group_by(['date_prod', 'api_no_10']).having(sum_oil >= 0.)\
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.aggregate(sum_oil))\
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.sort_by('date_prod')
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​
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# find the max production month per well in the wellset
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max_oil = expr.oil_monthly.max().name('oil_monthly')
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expr1 = expr.group_by('api_no_10').aggregate(max_oil)
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​
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expr2 = (expr.join(expr1, [expr.api_no_10 == expr1.api_no_10,\
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expr.oil_monthly == expr1.oil_monthly]))\
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[expr.api_no_10.name("api"), expr.oil_monthly.name("om2"), expr.date_prod.name("dp2")]
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​
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print(expr2.compile())
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This results in automatically generated SQL:
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WITH t0 AS (
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SELECT "date_prod", "api_no_10", log10(sum("oil_monthly")) AS oil_monthly
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FROM upstream_reservoir
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WHERE ("api_no_10" IN ('4201334998', '4212331782', '4212331826', '4201334965', '4212300017', '4201334329', '4228533451', '4212331164', '4201335467', '4201334930', '4212330405', '4201334947', '4212332354', '4201334978', '4201334917', '4201335379', '4201334812', '4212332207', '4228532777', '4201335112', '4212332076', '4212331839', '4212332014')) AND
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("oil_monthly" IS NOT NULL)
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GROUP BY date_prod, api_no_10
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HAVING log10(sum("oil_monthly")) >= 0.0
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ORDER BY "date_prod"
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)
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SELECT t0."api_no_10" AS api, t0."oil_monthly" AS om2, t0."date_prod" AS dp2
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FROM t0
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JOIN (
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SELECT "api_no_10", max("oil_monthly") AS oil_monthly
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FROM t0
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GROUP BY api_no_10
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) t1
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ON (t0."api_no_10" = t1."api_no_10") AND
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(t0."oil_monthly" = t1."oil_monthly")
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The result of the evaluation is by default a pandas dataframe, making it convenient to use Ibis inside other Python data analysis workflows.
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expr2.execute()
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​
api
om2
dp2
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4201335379
3.944927
2016-11-01
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4201335467
4.125253
2017-12-01
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4201334998
3.822887
2014-09-01
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4201334930
3.698709
2014-05-01
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4201334917
4.353243
2014-06-01
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4201334812
4.127299
2014-03-01
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4201334947
3.715502
2014-06-01
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4201334978
3.822887
2014-09-01
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4201335112
4.010766
2015-01-01
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4201334329
3.721068
2015-07-01
Although Ibis can output to pandas, the result is first materialized and could be transferred over the network to the client running Ibis (such as a Jupyter notebook in a browser on a laptop). Avoid using it for expressions that try to move a million rows to a browser.
Apache Arrow Support
Ibis depends on heavyai as a low-level connector. It can leverage some of the key features of pyomnisci, including output of query results to the Arrow format via the
select_ipcandselect_ipc_gpucalls.Building on this capability, the HeavyDB backend for Ibis can output query results using Apache Arrow to CPU or GPU memory (the latter by using the cudf dataframe library from NVIDIA). This makes it convenient to use with the GPU-based RAPIDS machine learning methods. Output to cudf is as simple as passing an extra couple of parameters to the
execute method in ibis, for example, as follows1
gdf = get_ts_by_period_station(zip_cd=94568).execute(gpu_device=3, ipc=True)
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RAPIDs is currently usable only with supported Nvidia GPUs and requires CUDA
Geospatial Operations
The HeavyDB backend for Ibis supports geospatial functions building directly on HeavyDB SQL support for these functions. These operations produce geopandas dataframes, allowing for usage in Python-based geospatial analytics workflows.
Here is an example of how a table that has geospatial data in HEAVY.AI, can output directly to a geopandas dataframe.
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heavyai_cli = ibis.heavyai.connect(
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host='metis.heavyai.com', user='heavyai', password='HyperInteractive',
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port=443, database='heavyai', protocol= 'https'
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)
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t = heavyai_cli.table('zipcodes_2017')
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df = t.head().execute()
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type(df)
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​
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#geopandas.geodataframe.GeoDataFrame
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User-Defined Functions
Using Ibis, you can create and use user-defined functions (UDFs) in Python that execute inside HeavyDB. This UDF framework leverages integration with Numba, a Just-in-Time (JIT) compiler backend for python, and produces lower-level code for a more performant execution path than within Python itself.
It also makes it easy to author UDFs in Python, and then make the UDFs usable in a SQL workflow.
Additional Resources
See the following Ibis documentation to get started:
- The Ibis User Guide---in particular, the Ibis for SQL programmers section, which shows how Ibis simplifies workflows involving analytical SQL.