Aggregate Functions (Frequency Estimation) , Window Functions
Combines (merges) input states into a single output state.
This allows scenarios where APPROX_TOP_K_ACCUMULATE is run over horizontal partitions of the same table, producing an algorithm state for each table partition. These states can later be combined using APPROX_TOP_K_COMBINE, producing the same output state as a single run of APPROX_TOP_K_ACCUMULATE over the entire table.
- See also:
APPROX_TOP_K_COMBINE( <state> [ , <counters> ] )
An expression that contains state information generated by a call to APPROX_TOP_K_ACCUMULATE.
This is the maximum number of distinct values that can be tracked at a time during the estimation process. For example, if
countersis set to 100000, then the algorithm tracks 100,000 distinct values, attempting to keep the 100,000 most frequent values.
The maximum number of
This returns information about the “state” of the top K calculation.
This state information is not usually useful by itself, but can be passed to
countersis defined, the output state uses the specified number of counters.
countersis not defined, all input states must have the same number of counters.
This example shows how to use the three related functions
This example uses more counters than distinct data values in order to get consistent results. In real-world applications, the number of distinct values is usually larger than the number of counters, so the approximations can vary.
This example generates one table with 8 rows that have values 1 - 8, and a second table with 8 rows that have values 5 - 12. Thus the most frequent values in the union of the two tables are the values 5-8, each of which has a count of 2.
Create a simple table and data:-- Create a sequence to use to generate values for the table. CREATE OR REPLACE SEQUENCE seq91; CREATE OR REPLACE TABLE sequence_demo (c1 INTEGER DEFAULT seq91.nextval, dummy SMALLINT); INSERT INTO sequence_demo (dummy) VALUES (0); -- Double the number of rows a few times, until there are 8 rows: INSERT INTO sequence_demo (dummy) SELECT dummy FROM sequence_demo; INSERT INTO sequence_demo (dummy) SELECT dummy FROM sequence_demo; INSERT INTO sequence_demo (dummy) SELECT dummy FROM sequence_demo;
Create a table that contains the “state” that represents the current approximate Top K information for the table named sequence_demo:CREATE OR REPLACE TABLE resultstate1 AS ( SELECT approx_top_k_accumulate(c1, 50) AS rs1 FROM sequence_demo);
Now create a second table and add data. (In a more realistic situation, the user could have loaded more data into the first table and divided the data into non-overlapping sets based on the time that the data was loaded.)CREATE OR REPLACE TABLE test_table2 (c1 INTEGER); -- Insert data. INSERT INTO test_table2 (c1) SELECT c1 + 4 FROM sequence_demo;
Get the “state” information for just the new data.CREATE OR REPLACE TABLE resultstate2 AS (SELECT approx_top_k_accumulate(c1, 50) AS rs1 FROM test_table2);
Combine the “state” information for the two batches of rows:CREATE OR REPLACE TABLE combined_resultstate (c1) AS SELECT approx_top_k_combine(rs1) AS apc1 FROM ( SELECT rs1 FROM resultstate1 UNION ALL SELECT rs1 FROM resultstate2 ) ;
Get the approximate Top K value of the combined set of rows:SELECT approx_top_k_estimate(c1, 4) FROM combined_resultstate;
Output:+------------------------------+ | APPROX_TOP_K_ESTIMATE(C1, 4) | |------------------------------| | [ | | [ | | 5, | | 2 | | ], | | [ | | 6, | | 2 | | ], | | [ | | 7, | | 2 | | ], | | [ | | 8, | | 2 | | ] | | ] | +------------------------------+