The purpose of this project is to provide an API for manipulating time series on top of Apache Spark. Functionality includes featurization using lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, and downsampling & interpolation. This has been tested on TB-scale of historical data and is unit tested for quality purposes.





GitHub Stars



Last Commit

21d ago









tempo - Time Series Utilities for Data Teams Using Databricks

Project Description

The purpose of this project is to make time series manipulation with Spark simpler. Operations covered under this package include AS OF joins, rolling statistics with user-specified window lengths, featurization of time series using lagged values, and Delta Lake optimization on time and partition fields.


Using the Project

Python - pip install in Databricks notebooks using:

%pip install dbl-tempo

Install locally using:

pip install dbl-tempo

Scala installation:

Once the jar is created (via the following instructions), upload the jar to Databricks for use in a notebook or job:

cd scala/tempo
sbt package

Starting Point: TSDF object, a wrapper over a Spark data frame

The entry point into all features for time series analysis in tempo is a TSDF object which wraps the Spark data frame. At a high level, a TSDF contains a data frame which contains many smaller time series, one per partition key. In order to create a TSDF object, a distinguished timestamp column much be provided in order for sorting purposes for public methods. Optionally, a sequence number and partition columns can be provided as the assumptive columns on which to create new features from. Below are the public methods available for TSDF transformation and enrichment.

Sample Reference Architecture for Capital Markets

Quickstart - Python

Data source is UCI public accelerometer data available at this URL https://archive.ics.uci.edu/ml/datasets/Heterogeneity+Activity+Recognition

0. Read in Data

from pyspark.sql.functions import * 

phone_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Phones_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))

from tempo import * 

phone_accel_tsdf = TSDF(phone_accel_df, ts_col="event_ts", partition_cols = ["User"])

1. Resample and Visualize

Sample usage:

Possible values for frequency include patterns such as 1 minute, 4 hours, 2 days or simply sec, min, day. For the accepted functions to aggregate data, options are 'floor', 'ceil', 'min', 'max', 'mean'. Custom functions will be available in a future release.

Note: You can upsample any missing values by using an option in the resample interface (fill = True)

# ts_col = timestamp column on which to sort fact and source table
# partition_cols - columns to use for partitioning the TSDF into more granular time series for windowing and sorting

resampled_sdf = phone_accel_tsdf.resample(freq='min', func='floor')
resampled_pdf = resampled_sdf.df.filter(col('event_ts').cast("date") == "2015-02-23").toPandas()

import plotly.graph_objs as go
import plotly.express as px
import pandas as pd

# Plotly figure 1
fig = px.line(resampled_pdf, x='event_ts', y='z',
              line_group="User", hover_name="User")
fig.update_layout(title='Phone Accelerometer Usage' , showlegend=False)


2. AS OF Join

This join uses windowing in order to select the latest record from a source table and merges this onto the base Fact table

from pyspark.sql.functions import * 

watch_accel_df = spark.read.format("csv").option("header", "true").load("dbfs:/home/tempo/Watch_accelerometer").withColumn("event_ts", (col("Arrival_Time").cast("double")/1000).cast("timestamp")).withColumn("x", col("x").cast("double")).withColumn("y", col("y").cast("double")).withColumn("z", col("z").cast("double")).withColumn("event_ts_dbl", col("event_ts").cast("double"))

watch_accel_tsdf = TSDF(watch_accel_df, ts_col="event_ts", partition_cols = ["User"])

# Applying AS OF join to TSDF datasets
joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="phone_accel").df

joined_df.show(10, False)

3. Skew Join Optimized AS OF Join

The purpose of the skew optimized as of join is to bucket each set of partition_cols to get the latest source record merged onto the fact table


ts_col = timestamp column for sorting partition_cols = partition columns for defining granular time series for windowing and sorting tsPartitionVal = value to break up each partition into time brackets fraction = overlap fraction right_prefix = prefix used for source columns when merged into fact table

joined_df = watch_accel_tsdf.asofJoin(phone_accel_tsdf, right_prefix="watch_accel", tsPartitionVal = 10, fraction = 0.1).df
joined_df.show(10, False)

4 - Approximate Exponential Moving Average

The approximate exponential moving average uses an approximation of the form EMA = e * lag(col,0) + e * (1 - e) * lag(col, 1) + e * (1 - e)^2 * lag(col, 2) to define a rolling moving average based on exponential decay.


window = number of lagged values to compute for moving average

ema_trades = watch_accel_tsdf.EMA("x", window = 50).df
ema_trades.show(10, False)

5 - Simple Moving Average

Method for computing rolling statistics based on the distinguished timestamp column


rangeBackWindowSecs = number of seconds to look back

moving_avg = watch_accel_tsdf.withRangeStats("y", rangeBackWindowSecs=600).df
moving_avg.select('event_ts', 'x', 'y', 'z', 'mean_y').show(10, False)

Project Support

Please note that all projects in the /databrickslabs github account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects.

Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.

Project Setup

After cloning the repo, it is highly advised that you create a virtual environment to isolate and manage packages for this project, like so:

python -m venv <path to project root>/venv

You can then install the required modules via pip:

pip install requirements.txt

Building the Project

Once in the main project folder, build into a wheel using the following command:

python setup.py bdist_wheel

Releasing the Project

Instructions for how to release a version of the project

Rate & Review

Great Documentation0
Easy to Use0
Highly Customizable0
Bleeding Edge0
Responsive Maintainers0
Poor Documentation0
Hard to Use0
Unwelcoming Community0