Apache Spark and Time-Series Analytics

High-level instructions for loading data from QuestDB to Spark and back.

What is Spark?

Apache Spark is an analytics engine for large-scale data engineering and stream processing, well-known in the big data landscape. It is suitable for executing data engineering, data science, and machine learning on single-node machines or clusters.

QuestDB Spark integration

A typical Spark application processes data in the following steps:

  1. Loading data from different sources
  2. Transforming and analyzing the data
  3. Saving the result to a data storage

Our example demonstrates these steps using QuestDB as the data source and storage. It loads data from QuestDB into a Spark Dataframe; then the data is enriched with new features, and eventually, it is written back into QuestDB.

Prerequisites

  • Package manager: This depends on your choice of OS. The below instructions are for macOS using Homebrew.

  • QuestDB: An instance must be running and accessible. Not running? Checkout the quick start.

Installing Apache Spark

Spark can be installed and set up in many ways, depending on requirements. Typically, it is part of a Big Data stack, installed on multiple nodes with an external cluster manager, such as Yarn or Apache Mesos. In this tutorial, we will work with a single-node standalone Spark installation.

Spark has a multi-language environment. It is written in Scala, runs on the Java Virtual Machine, and also integrates with R and Python. Our example is written using Python. By running the below commands Spark will be installed with all required dependencies:

brew install openjdk@11
brew install python@3.10
brew install scala
brew install apache-spark

The exact versions used for this example:

openjdk@11 11.0.12
python@3.10 3.10.10_1
scala 3.2.2
apache-spark 3.3.2

Installing the JDBC driver

Spark communicates with QuestDB via JDBC, connecting to its Postgres Wire Protocol endpoint. This requires the Postgres JDBC driver to be present.

  • Create a working directory:
mkdir sparktest
cd sparktest
  • Download the JDBC driver from here into the working directory. The exact version used for this example:
postgresql-42.5.1.jar

Setting up database tables

First, start QuestDB. If you are using Docker run the following command:

docker run -p 9000:9000 -p 8812:8812 questdb/questdb:8.0.0

The port mappings allow us to connect to QuestDB's REST and PostgreSQL Wire Protocol endpoints. The former is required for opening the Web Console, and the latter is used by Spark to connect to the database.

Open the Web Console in your browser at http://localhost:9000.

Run the following SQL commands using the console:


CREATE TABLE trades (
symbol SYMBOL,
side SYMBOL,
price DOUBLE,
amount DOUBLE,
timestamp TIMESTAMP
) timestamp (timestamp) PARTITION BY DAY;

CREATE TABLE trades_enriched (
symbol SYMBOL,
volume DOUBLE,
mid DOUBLE,
ts TIMESTAMP,
ma10 DOUBLE,
std DOUBLE
) timestamp (ts) PARTITION BY DAY;

INSERT INTO trades SELECT * FROM (
SELECT 'BTC-USD' symbol,
rnd_symbol('buy', 'sell') side,
rnd_double() * 10000 price,
rnd_double() amount,
timestamp_sequence(1677628800000000, 10000000) ts
FROM long_sequence(25920)
) timestamp (ts);

The INSERT command generates 3 days' worth of test data, and stores it in the trades table.

Feature engineering examples

Save the below Python code into a file called sparktest.py inside the working directory:

from pyspark.sql import SparkSession
from pyspark.sql.window import Window
from pyspark.sql.functions import avg, stddev, when

# create Spark session
spark = SparkSession.builder.appName("questdb_test").getOrCreate()

# load 1-minute aggregated trade data into the dataframe
df = spark.read.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:8812/questdb") \
.option("driver", "org.postgresql.Driver") \
.option("user", "admin").option("password", "quest") \
.option("dbtable", "(SELECT symbol, sum(amount) as volume, "
"round((max(price)+min(price))/2, 2) as mid, "
"timestamp as ts "
"FROM trades WHERE symbol = 'BTC-USD' "
"SAMPLE BY 1m ALIGN to CALENDAR) AS mid_prices") \
.option("partitionColumn", "ts") \
.option("numPartitions", "3") \
.option("lowerBound", "2023-03-01T00:00:00.000000Z") \
.option("upperBound", "2023-03-04T00:00:00.000000Z") \
.load()

# extract new features, clean data
window_10 = Window.partitionBy(df.symbol).rowsBetween(-10, Window.currentRow)
df = df.withColumn("ma10", avg(df.mid).over(window_10))
df = df.withColumn("std", stddev(df.mid).over(window_10))
df = df.withColumn("std", when(df.std.isNull(), 0.0).otherwise(df.std))

# save the data as 'trades_enriched', overwrite if already exists
df.write.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:8812/questdb") \
.option("driver", "org.postgresql.Driver") \
.option("user", "admin").option("password", "quest") \
.option("dbtable", "trades_enriched") \
.option("truncate", True) \
.option("createTableColumnTypes", "volume DOUBLE, mid DOUBLE, ma10 DOUBLE, std DOUBLE") \
.save(mode="overwrite")

This Spark application loads aggregated data from the trades table into a Dataframe, then adds two new features, a 10-minute moving average and the standard deviation. Finally, it writes the enriched data back into QuestDB and saves it to the trades_enriched table.

Run the example

Submit the application to Spark for execution using spark-submit:

spark-submit --jars postgresql-42.5.1.jar sparktest.py

The example requires the JDBC driver at runtime. This dependency is submitted to Spark using the --jars option.

After the execution is completed, we can check the content of the trades_enriched table:

SELECT * FROM trades_enriched;

The enriched data should be displayed in the Web Console.

See also

For a more detailed explanation of the QuestDB Spark integration, please also see our tutorial Integrate Apache Spark and QuestDB for Time-Series Analytics.