Returns the approximate most frequent values and their estimated frequency for the given Space-Saving state. (For more information about the Space-Saving summary, see Estimating Frequent Values.)
Thus, APPROX_TOP_K_ESTIMATE(APPROX_TOP_K_ACCUMULATE(…)) is equivalent to APPROX_TOP_K(…).
APPROX_TOP_K_ESTIMATE( <state> [ , <k> ] )
- An expression that contains state information generated by a call to APPROX_TOP_K_ACCUMULATE or APPROX_TOP_K_COMBINE.
The number of values whose counts you want approximated. For example, if you want to see the top 10 most common values, then set
kis omitted, the default is
The maximum value is
100000(100,000), and is automatically reduced if items cannot fit in the output.
Returns a value of type
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 | | ] | | ] | +------------------------------+