Fast JDBC access in Python using pyarrow.jvm

While most databases are accessible via ODBC where we have an efficient way via turbodbc to turn results into a pandas.DataFrame, there are nowadays a lot of databases that either only come solely with a JDBC driver or the non-JDBC drivers are not part of free or open-source offering. To access these databases, you can use JayDeBeApi which is using JPype to call the JDBC driver. JPype starts a JVM inside the Python process and exposes the Java APIs as plain Python objects. While the convenience of use is really nice, this Java-Python bridge sadly comes at a high serialisation cost.

One of the main goals of Apache Arrow is to remove the serialisation cost of tabular data between different languages. A typical example where this already is successfully used is the Scala-Python bridge in PySpark. Here the communication between the JVM and Python is done via Py4J, a bridge between Python and JVM process. As there are multiple processes involved, the serialisation cost is reduced but communication and data copy between the ecosystems still exists.

In the following, we want to present an alternative approach to retrieve data via JDBC where the overhead between the JVM and pandas is kept as minimal as possible. This includes retrieving the whole data on the JVM side, transforming it to an Arrow Record Batch and then passing the memory pointer to that Record Batch over to Python. The important detail here is that we only pass a pointer to the data to Python, not the data itself.

Benchmark setup

In this benchmark, we will use Apache Drill as the database using its official JDBC driver. For the data, we will use the January 2017 Yellow Cab New York City trip data converted to Parquet. We start Drill in its embedded mode using ./bin/drill-embedded. There we can already peak into the data using

SELECT * FROM dfs.`/…/data/yellow_tripdata_2016-01.parquet` LIMIT 1

As the main aspect here is to show how to access databases using JDBC in Python, we will use JayDeBeApi now to connect to this running Drill instance. Therefore we start a JVM with jpype and then connect using jaydebeapi and the drill-jdbc-all-1.16.0.jar JAR to the database. For the JDBC connections, it is important that we have either a classpath with all Java dependencies or as in this case, a JAR that already bundles all dependencies. Finally, we execute the query and use the result to construct a pandas.DataFrame.

import jaydebeapi
import jpype
import os

classpath = os.path.join(os.getcwd(), "apache-drill-1.16.0/jars/jdbc-driver/drill-jdbc-all-1.16.0.jar")
jpype.startJVM(jpype.getDefaultJVMPath(), f"-Djava.class.path={classpath}")
conn = jaydebeapi.connect('org.apache.drill.jdbc.Driver', 'jdbc:drill:drillbit=')
cursor = conn.cursor()

query = """
    SELECT * 
    FROM dfs.`/…/data/yellow_tripdata_2016-01.parquet`
    LIMIT 1

columns = [c[0] for c in cursor.description]
data = cursor.fetchall()
df = pd.DataFrame(data, columns=columns)

To measure the performance, we have tried initially to run the full query to measure the retrieval performance but as this didn’t finish after 10min, we reverted to running the SELECT query with different LIMIT sizes. This lead to the following response times on my laptop (mean ± std. dev. of 7 runs):

LIMIT n Time
10000 7.11 s ± 58.6 ms
 100000 1min 9s ± 1.07 s
1000000 11min 31s ± 4.76 s

Out of curiosity, we have retrieved the full result set once and this came down to an overall time of 2h 42min 59s on a warm JVM.

pyarrow.jvm and combined jar

As the above times were quite frustrating, we have high hopes that using Apache Arrow could bring a decent speedup for this operation. To use Apache Arrow Java and the Drill ODBC driver together, we need to bundle both together on the JVM classpath. The simplest way to do this is generate a new JAR that includes all dependencies using a build tool like Apache Maven. With the following pom.xml you get a fat JAR using mvn assembly:single. It is important here that your Apache Arrow Java version matches the pyarrow version, in this case here, both are at 0.15.1. It might still work when they differ but as there is limited API stability between the two implementations, this could otherwise lead to crashes.

<project xmlns="" xmlns:xsi=""






After the JAR has been built, we now want to start the JVM with it loaded. Sadly, jpype has the limitation that you need to restart your Python process when you want to restart the JVM with different parameters. We thus adjust the JVM startup command to:

classpath = os.path.join(os.getcwd(), "all-jar/target/drill-odbc-0.1-SNAPSHOT-jar-with-dependencies.jar")

To use Apache Arrow Java to retrieve the result, we need to instantiate a RootAllocator that is used in Arrow Java to allocate the off-heap memory and also construct a DriverManager instance to connect to the database.

ra = jpype.JPackage("org").apache.arrow.memory.RootAllocator(sys.maxsize)
dm = jpype.JPackage("java").sql.DriverManager
connection = dm.getConnection("jdbc:drill:drillbit=")

Once this is setup, we can use the Java method sqlToArrow to query a database using JDBC, retrieve the result and convert it to an Arrow RecordBatch on the Java side. With the helper pyarrow.jvm.record_batch we can take the jpype reference to the Java object, extract the memory address of the RecordBatch and create a matching Python pyarrow.RecordBatch object that points to the same memory.

batch = jpype.JPackage("org").apache.arrow.adapter.jdbc.JdbcToArrow.sqlToArrow(

df = pyarrow.jvm.record_batch(batch).to_pandas()

Using these commands, we can now execute the same queries again and compare them to the jaydebeapi times:

LIMIT n Time (JayDeBeApi) Time (pyarrow.jvm) Speedup
10000 7.11 s ± 58.6 ms 165 ms ± 5.86 ms 43x
 100000 1min 9s ± 1.07 s 538 ms ± 29.6 ms 128x
1000000 11min 31s ± 4.76 s 5.05 s ± 596 ms 136x

With the pyarrow.jvm approach, we not get similar times to turbodbc.fetchallarrow() on other databases that come with an open ODBC driver. This also leads to the retrieval of the whole being a more sane 50.2 s instead of the hours-long wait with jaydebeapi.


By moving the row-to-columnar conversion to the JVM and avoiding to create intermediate Python objects before creating a pandas.DataFrame again, we can speedup the retrieval times for JDBC drivers in Python by over *100x*. As a user, you need to change your calls to jaydebeapi to the Apache Arrow Java API and pyarrow.jvm`. Additionally, you will have to take care that the Apache Arrow Java and the JDBC drivers are on the Java classpath. By using a common Java build tool, this can be achieved by simply declaring them as dependencies of a dummy package.