UDF concepts

Depending on your use case, you might write all-new functions, reuse Java UDFs that you have already written for Hive, or port Hive Java UDF code to higher-performance native Impala UDFs in C++. You can code either scalar functions for producing results one row at a time, or more complex aggregate functions for doing analysis across. The following sections discuss these different aspects of working with UDFs.

UDFs and UDAFs

Depending on your use case, the user-defined functions (UDFs) you write might accept or produce different numbers of input and output values:

  • The most general kind of user-defined function (the one typically referred to by the abbreviation UDF) takes a single input value and produces a single output value. When used in a query, it is called once for each row in the result set. For example:
    select customer_name, is_frequent_customer(customer_id) from customers;
    select obfuscate(sensitive_column) from sensitive_data;
  • A user-defined aggregate function (UDAF) accepts a group of values and returns a single value. You use UDAFs to summarize and condense sets of rows, in the same style as the built-in COUNT, MAX(), SUM(), and AVG() functions. When called in a query that uses the GROUP BY clause, the function is called once for each combination of GROUP BY values. For example:
    -- Evaluates multiple rows but returns a single value.
    select closest_restaurant(latitude, longitude) from places;
    
    -- Evaluates batches of rows and returns a separate value for each batch.
    select most_profitable_location(store_id, sales, expenses, tax_rate, depreciation) from franchise_data group by year;
  • Currently, Impala does not support other categories of user-defined functions, such as user-defined table functions (UDTFs) or window functions.
Native Impala UDFs

Impala supports UDFs written in C++, in addition to supporting existing Hive UDFs written in Java. Cloudera recommends using C++ UDFs because the compiled native code can yield higher performance, with UDF execution time often 10x faster for a C++ UDF than the equivalent Java UDF.

Using Hive UDFs with Impala

Impala can run Java-based user-defined functions (UDFs), originally written for Hive, with no changes, subject to the following conditions:

  • The parameters and return value must all use scalar data types supported by Impala. For example, complex or nested types are not supported.
  • Hive/Java UDFs must extend org.apache.hadoop.hive.ql.exec.UDF class.
  • Currently, Hive UDFs that accept or return the TIMESTAMP type are not supported.
  • Prior to Impala 2.5 the return type must be a “Writable” type such as Text or IntWritable, rather than a Java primitive type such as String or int. Otherwise, the UDF returns NULL. In Impala 2.5 and higher, this restriction is lifted, and both UDF arguments and return values can be Java primitive types.
  • Hive UDAFs and UDTFs are not supported.
  • Typically, a Java UDF will execute several times slower in Impala than the equivalent native UDF written in C++.
  • In Impala 2.5 and higher, you can transparently call Hive Java UDFs through Impala, or call Impala Java UDFs through Hive. This feature does not apply to built-in Hive functions. Any Impala Java UDFs created with older versions must be re-created using new CREATE FUNCTION syntax, without any signature for arguments or the return value.

To take full advantage of the Impala architecture and performance features, you can also write Impala-specific UDFs in C++.

For background about Java-based Hive UDFs, see the Hive documentation for UDF. For examples or tutorials for writing such UDFs, search the web for related blog posts.

The ideal way to understand how to reuse Java-based UDFs (originally written for Hive) with Impala is to take some of the Hive built-in functions (implemented as Java UDFs) and take the applicable JAR files through the UDF deployment process for Impala, creating new UDFs with different names:

  1. Take a copy of the Hive JAR file containing the Hive built-in functions.
  2. Use jar tf jar_file to see a list of the classes inside the JAR. You will see names like org/apache/hadoop/hive/ql/udf/UDFLower.class and org/apache/hadoop/hive/ql/udf/UDFOPNegative.class. Make a note of the names of the functions you want to experiment with. When you specify the entry points for the Impala CREATE FUNCTION statement, change the slash characters to dots and strip off the .class suffix, for example org.apache.hadoop.hive.ql.udf.UDFLower and org.apache.hadoop.hive.ql.udf.UDFOPNegative.
  3. Copy that file to an HDFS location that Impala can read. (In the examples here, we renamed the file to hive-builtins.jar in HDFS for simplicity.)
  4. For each Java-based UDF that you want to call through Impala, issue a CREATE FUNCTION statement, with a LOCATION clause containing the full HDFS path of the JAR file, and a SYMBOL clause with the fully qualified name of the class, using dots as separators and without the .class extension. Remember that user-defined functions are associated with a particular database, so issue a USE statement for the appropriate database first, or specify the SQL function name as db_name.function_name. Use completely new names for the SQL functions, because Impala UDFs cannot have the same name as Impala built-in functions.
  5. Call the function from your queries, passing arguments of the correct type to match the function signature. These arguments could be references to columns, arithmetic or other kinds of expressions, the results of CAST functions to ensure correct data types, and so on.
Java UDF example: Reusing lower() function

For example, the following impala-shell session creates an Impala UDF my_lower() that reuses the Java code for the Hive lower(): built-in function. We cannot call it lower() because Impala does not allow UDFs to have the same name as built-in functions. From SQL, we call the function in a basic way (in a query with no WHERE clause), directly on a column, and on the results of a string expression:

[localhost:21000] > create database udfs;
[localhost:21000] > use udfs;
localhost:21000] > create function lower(string) returns string location '/user/hive/udfs/hive.jar' symbol='org.apache.hadoop.hive.ql.udf.UDFLower';
ERROR: AnalysisException: Function cannot have the same name as a builtin: lower
[localhost:21000] > create function my_lower(string) returns string location '/user/hive/udfs/hive.jar' symbol='org.apache.hadoop.hive.ql.udf.UDFLower';
[localhost:21000] > select my_lower('Some String NOT ALREADY LOWERCASE');
+----------------------------------------------------+
| udfs.my_lower('some string not already lowercase') |
+----------------------------------------------------+
| some string not already lowercase                  |
+----------------------------------------------------+
Returned 1 row(s) in 0.11s
[localhost:21000] > create table t2 (s string);
[localhost:21000] > insert into t2 values ('lower'),('UPPER'),('Init cap'),('CamelCase');
Inserted 4 rows in 2.28s
[localhost:21000] > select * from t2;
+-----------+
| s         |
+-----------+
| lower     |
| UPPER     |
| Init cap  |
| CamelCase |
+-----------+
Returned 4 row(s) in 0.47s
[localhost:21000] > select my_lower(s) from t2;
+------------------+
| udfs.my_lower(s) |
+------------------+
| lower            |
| upper            |
| init cap         |
| camelcase        |
+------------------+
Returned 4 row(s) in 0.54s
[localhost:21000] > select my_lower(concat('ABC ',s,' XYZ')) from t2;
+------------------------------------------+
| udfs.my_lower(concat('abc ', s, ' xyz')) |
+------------------------------------------+
| abc lower xyz                            |
| abc upper xyz                            |
| abc init cap xyz                         |
| abc camelcase xyz                        |
+------------------------------------------+
Returned 4 row(s) in 0.22s
Java UDF example: Reusing negative() function

Here is an example that reuses the Hive Java code for the negative() built-in function. This example demonstrates how the data types of the arguments must match precisely with the function signature. At first, we create an Impala SQL function that can only accept an integer argument. Impala cannot find a matching function when the query passes a floating-point argument, although we can call the integer version of the function by casting the argument. Then we overload the same function name to also accept a floating-point argument.

[localhost:21000] > create table t (x int);
[localhost:21000] > insert into t values (1), (2), (4), (100);
Inserted 4 rows in 1.43s
[localhost:21000] > create function my_neg(bigint) returns bigint location '/user/hive/udfs/hive.jar' symbol='org.apache.hadoop.hive.ql.udf.UDFOPNegative';
[localhost:21000] > select my_neg(4);
+----------------+
| udfs.my_neg(4) |
+----------------+
| -4             |
+----------------+
[localhost:21000] > select my_neg(x) from t;
+----------------+
| udfs.my_neg(x) |
+----------------+
| -2             |
| -4             |
| -100           |
+----------------+
Returned 3 row(s) in 0.60s
[localhost:21000] > select my_neg(4.0);
ERROR: AnalysisException: No matching function with signature: udfs.my_neg(FLOAT).
[localhost:21000] > select my_neg(cast(4.0 as int));
+-------------------------------+
| udfs.my_neg(cast(4.0 as int)) |
+-------------------------------+
| -4                            |
+-------------------------------+
Returned 1 row(s) in 0.11s
[localhost:21000] > create function my_neg(double) returns double location '/user/hive/udfs/hive.jar' symbol='org.apache.hadoop.hive.ql.udf.UDFOPNegative';
[localhost:21000] > select my_neg(4.0);
+------------------+
| udfs.my_neg(4.0) |
+------------------+
| -4               |
+------------------+
Returned 1 row(s) in 0.11s

You can find the sample files mentioned here in the Impala github repo.