![]() Gives the approximate quantile using T-Digest. Gives the approximate count of distinct elements using HyperLogLog. The table below shows the available approximate aggregate functions. Returns the minimum value present in arg.Ĭalculates the product of all tuples in argĬoncatenates the column string values with a separatorĬalculates the sum value for all tuples in arg. Returns the maximum value present in arg. Returns a LIST containing all the values of a column. Returns a LIST of STRUCTs with the fields bucket and count. Returns true if any input value is true, otherwise false.Ĭalculates the average using a more accurate floating point summation (Kahan Sum).Ĭalculates the sum using a more accurate floating point summation (Kahan Sum).Ĭalculates the geometric mean for all tuples in arg. Returns true if every input value is true, otherwise false. Returns a bitstring with bits set for each distinct value. Returns the bitwise XOR of all bits in a given expression. Returns the bitwise OR of all bits in a given expression. Returns the bitwise AND of all bits in a given expression. Calculates the arg expression at that row.Ĭalculates the average value for all tuples in arg. Calculates the arg expression at that row.įinds the row with the minimum val. Returns the first non-null value from arg.įinds the row with the maximum val. The table below shows the available general aggregate functions. These can be made deterministic by ordering the arguments.įor order-insensitive aggregates, this clause is parsed and applied, which is inefficient, but still produces the same result. (e.g., first, last, list and string_agg). Usually this is not important, but there are some order-sensitive aggregates that can have indeterminate results When the ORDER BY clause is provided, the values being aggregated are sorted before applying the function. This is typically used in combination with the COUNT aggregate to get the number of distinct elements but it can be used together with any aggregate function in the system. When the DISTINCT clause is provided, only distinct values are considered in the computation of the aggregate. As such, aggregates can only be used in the SELECT and HAVING clauses of a SQL query. Aggregates are different from scalar functions and window functions because they change the cardinality of the result. This can be removed if required, but unless it has explicitly been removed, you will be able to leverage it within your queries.Aggregates are functions that combine multiple rows into a single value. How does this work? By default, every row in SQLite has a special column, usually called the rowid, that uniquely identifies that row within the table. We can take advantage of SQLite’s rowid: SELECT * FROM Pets But SQLite won’t let us update the CTE like that.įortunately, the next two options can be modified to perform a delete. In some other DBMSs (in SQL Server at least), we can replace the last SELECT * with DELETE to delete the duplicate rows from the table. ![]() This query can be useful for showing how many rows will be removed from the table in a de-duping operation. If there are three identical rows, it returns two, and so on. So if there are two identical rows, it returns one of them. This returns just the excess rows from the matching duplicates. We can use the above query as a common table expression: WITH cte AS the numbering will start at 1 for the first row in each partition). When we specify partitions for the result set, each partition causes the numbering to start over again (i.e. The PARTITION BY clause divides the result set produced by the FROM clause into partitions to which the function is applied. If we only want the duplicate rows listed, we can use the the HAVING clause to return only rows with a count of greater than 1: SELECTĪnother option is to use the ROW_NUMBER() window function: SELECT We can order it by count in descending order, so that the rows with the most duplicates appear first: SELECT This tells us whether a row is unique (with a count of 1) or a duplicate (with a count greater than 1). Here, we grouped the rows by all columns, and returned the row count of each group. We can use the following query to see how many rows are duplicates: SELECT That’s because all three columns contain the same values in each duplicate row. The first two rows are duplicates, as are the last three rows. Suppose we have a table with the following data: SELECT * FROM Pets Here, the duplicate rows contain duplicate values across all columns, including the ID column. The following queries can be used to return duplicate rows in SQLite.
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