User-defined Functions


Apache AsterixDB supports three languages for writing user-defined functions (UDFs): SQL++, Java, and Python A user can encapsulate data processing logic into a UDF and invoke it later repeatedly. For SQL++ functions, a user can refer to SQL++ Functions for their usages. This document will focus on UDFs in languages other than SQL++

Endpoints and Authentication

The UDF API endpoint used to deploy functions is not enabled by default until authentication has been configured properly. Even if the endpoint is enabled, it is only accessible on the loopback interface on each NC to restrict access.

To enable it, we need to set the path to the credential file and populate it with our username and password.

The credential file is a simple /etc/passwd style text file with usernames and corresponding bcrypt hashed and salted passwords. You can populate this on your own if you would like, but the asterixhelper utility can write the entries as well. We can invoke asterixhelper like so:

$ bin/asterixhelper -u admin -p admin -cp opt/local/conf add_credential

Then, in your cc.conf, in the [cc] section, add the correct credential.file path

address =
credential.file = conf/passwd

Now,restart the cluster if it was already started to allow the Cluster Controller to find the new credentials.

Installing a Java UDF Library

To install a UDF package to the cluster, we need to send a Multipart Form-data HTTP request to the /admin/udf endpoint of the CC at the normal API port (19004 by default). Any suitable tool will do, but for the example here I will use curl which is widely available.

For example, to install a library with the following criteria:

  • udfs dataverse name
  • with a new Library name of testlib
  • from in the present working directory
  • to the cluster at localhost with API port 19004 of the Asterix CC
  • with credentials being a username and password of admin:admin

we would execute

curl -v -u admin:admin -X POST -F 'data=@./' -F 'type=java' localhost:19004/admin/udf/udfs/testlib

Any response other than 200 indicates an error in deployment.

In the AsterixDB source release, we provide several sample UDFs that you can try out. You need to build the AsterixDB source to get the compiled UDF package. It can be found under the asterix-external-data sub-project under the path asterixdb/asterix-external-data/src/test/java/org/apache/asterix/external/library. After compilation, the UDFs will be packed in a zip file at asterixdb/asterix-external-data/target/asterix-external-data-$ which you can use to upload to the AsterixDB cluster.

Assuming that these UDFs have been installed into the testlib library inudfs dataverse, here is an example that uses the sample UDF mysum to compute the sum of two input integers.

USE udfs;

CREATE FUNCTION mysum(a: int32, b: int32)
  AS "org.apache.asterix.external.library.MySumFactory" AT testlib;

Creating a Python UDF

Python UDFs need to be rolled into a shiv package with all their dependencies. By default AsterixDB will use the Python interpreter located at /usr/bin/python3. This can be changed in the cluster config [common] section using the python.path configuration variable.

First, let’s devise a function that we would like to use in AsterixDB,

import os
from typing import Tuple
class sent_model:

    def __init__(self):
        good_words = os.path.join(os.path.dirname(__file__), 'good.txt')
        with open(good_words) as f:
            self.whitelist =

    def sentiment(self, arg: Tuple[str])-> str:
        words = arg[0].split()
        for word in words:
            if word in self.whitelist:
                return 'great'

        return 'eh'

Furthermore, let’s assume ‘good.txt’ contains the following entries


Now, in the module directory, execute shiv with all the dependencies of the module listed. We don’t actually use scikit-learn here (our method is obviously better!), but it’s just included as an example of a real dependency.

shiv -o lib.pyz --site-packages . scikit-learn

Then, deploy it the same as the Java UDF was, with the library name pylib in udfs dataverse

curl -v -u admin:admin -X POST -F 'data=@./lib.pyz' -F 'type=python' localhost:19002/admin/udf/udfs/pylib

With the library deployed, we can define a function within it for use. For example, to expose the Python function sentiment in the module sentiment_mod in the class sent_model, the CREATE FUNCTION would be as follows

USE udfs;

CREATE FUNCTION sentiment(a)
  AS "sentiment_mod", "sent_model.sentiment" AT pylib;

By default, AsterixDB will treat all external functions as deterministic. It means the function must return the same result for the same input, irrespective of when or how many times the function is called on that input. This particular function behaves the same on each input, so it satisfies the deterministic property. This enables better optimization of queries including this function. If a function is not deterministic then it should be declared as such by using a WITH sub-clause:

USE udfs;

CREATE FUNCTION sentiment(text)
  AS "sentiment_mod", "sent_model.sentiment" AT pylib
  WITH { "deterministic": false }

With the function now defined, it can then be used as any other scalar SQL++ function would be. For example:

USE udfs;

  {"id":1, "msg":"spam is great"},
  {"id":2, "msg":"i will not eat green eggs and ham"},
  {"id":3, "msg":"bacon is better"}

SELECT t.msg as msg, sentiment(t.msg) as sentiment
FROM Tweets t;

Python Type Mappings

Currently only a subset of AsterixDB types are supported in Python UDFs. The supported types are as follows:

  • Integer types (int8,16,32,64)
  • Floating point types (float, double)
  • String
  • Boolean
  • Arrays, Sets (cast to lists)
  • Objects (cast to dict)

Unsupported types can be cast to these in SQL++ first in order to be passed to a Python UDF

Execution Model For UDFs

AsterixDB queries are deployed across the cluster as Hyracks jobs. A Hyracks job has a lifecycle that can be simplified for the purposes of UDFs to - A pre-run phase which allocates resources, open - The time during which the job has data flowing through it, nextFrame - Cleanup and shutdown in close.

If a SQL++ function is defined as a member of a class in the library, the class will be instantiated during open. The class will exist in memory for the lifetime of the query. Therefore if your function needs to reference files or other data that would be costly to load per-call, making it a member variable that is initialized in the constructor of the object will greatly increase the performance of the SQL++ function.

For each function invoked during a query, there will be an independent instance of the function per data partition. This means that the function must not assume there is any global state or that it can assume things about the layout of the data. The execution of the function will be parallel to the same degree as the level of data parallelism in the cluster.

After initialization, the function bound in the SQL++ function definition is called once per tuple during the query execution (i.e. nextFrame). Unless the function specifies null-call in the WITH clause, NULL values will be skipped.

At the close of the query, the function is torn down and not re-used in any way. All functions should assume that nothing will persist in-memory outside of the lifetime of a query, and any behavior contrary to this is undefined.

Attaching a UDF on Data Feeds

In Data Ingestion using feeds, we introduced an efficient way for users to get data into AsterixDB. In some use cases, users may want to pre-process the incoming data before storing it into the dataset. To meet this need, AsterixDB allows the user to attach a UDF onto the ingestion pipeline. Following the example in Data Ingestion, here we show an example of how to attach a UDF that extracts the user names mentioned from the incoming Tweet text, storing the processed Tweets into a dataset.

We start by creating the datatype and dataset that will be used for the feed and UDF. One thing to keep in mind is that data flows from the feed to the UDF and then to the dataset. This means that the feed’s datatype should be the same as the input type of the UDF, and the output datatype of the UDF should be the same as the dataset’s datatype. Thus, users should make sure that their datatypes are consistent in the UDF configuration. Users can also take advantage of open datatypes in AsterixDB by creating a minimum description of the data for simplicity. Here we use open datatypes:

USE udfs;

    id: int64

CREATE DATASET ProcessedTweets(TweetType) PRIMARY KEY id;

As the TweetType is an open datatype, processed Tweets can be stored into the dataset after they are annotated with an extra attribute. Given the datatype and dataset above, we can create a Twitter Feed with the same datatype. Please refer to section Data Ingestion if you have any trouble in creating feeds.

USE udfs;

  "adapter-name": "push_twitter",
  "type-name": "TweetType",
  "format": "twitter-status",
  "consumer.key": "************",
  "consumer.secret": "************",
  "access.token": "**********",
  "access.token.secret": "*************"

Then we define the function we want to apply to the feed

USE udfs;

CREATE FUNCTION addMentionedUsers(t: TweetType)
  AS "org.apache.asterix.external.library.AddMentionedUsersFactory" AT testlib
  WITH { "resources": { "textFieldName": "text" } };

After creating the feed, we attach the UDF onto the feed pipeline and start the feed with following statements:

USE udfs;

CONNECT FEED TwitterFeed TO DATASET ProcessedTweets APPLY FUNCTION addMentionedUsers;

START FEED TwitterFeed;

You can check the annotated Tweets by querying the ProcessedTweets dataset:

SELECT * FROM ProcessedTweets LIMIT 10;

Installing a user-defined Feed Adapter

First, upload a zip file packaged the same way as a Java UDF, but also containing the adapter you would like to use. Next, issue a CREATE ADAPTER statement referencing the class name. For example:

  AS "org.apache.asterix.external.library.adapter.TestTypedAdapterFactory" AT testlib;

Then, the adapter can be used like any other adapter in a feed.

  "adapter-name": "TweetAdapter",
  "type-name" : "TweetType",
  "num_output_records": 4

Unstalling an UDF Library

If you want to uninstall the UDF library, simply issue a DELETE against the endpoint you POSTed against once all functions declared with the library are removed. First we’ll drop the function we declared earlier:

USE udfs;
DROP FUNCTION mysum(a,b);

Then issue the proper DELETE request

curl -u admin:admin -X DELETE localhost:19002/admin/udf/udfs/testlib

The library will also be dropped if you drop the dataverse entirely.