STDDEV_SAMP

Returns the statistical standard deviation of all values in <number-expression>, evaluated for each row remaining in the group and defined as the square root of the VAR_SAMP.

When you use STDDEV_SAMP, be aware of the following:

When the input set has no non-null data, STDDEV_SAMP returns NULL.
If you don't use the OVER clause, STDDEV_SAMP is calculated as an aggregate function. In this case, the aggregate query must contain a GROUP BY clause on a monotonic expression based on ROWTIME that groups the stream into finite rows. Otherwise, the group is the infinite stream, and the query will never complete and no rows will be emitted. For more information, see Aggregate Functions.
A windowed query that uses a GROUP BY clause processes rows in a tumbling window. For more information, see Tumbling Windows (Aggregations Using GROUP BY).
If you use the OVER clause, STDDEV_SAMP is calculated as an analytic function. For more information, see Analytic Functions.
A windowed query that uses an OVER clause processes rows in a sliding window. For more information, see Sliding Windows
STD_DEV is an alias of STDDEV_SAMP.

Syntax


 STDDEV_SAMP ( [DISTINCT | ALL] number-expression )

Parameters

ALL

Includes duplicate values in the input set. ALL is the default.

DISTINCT

Excludes duplicate values in the input set.

Examples

Example Dataset

The examples following are based on the sample stock dataset that is part of the Getting Started Exercise in the Amazon Kinesis Analytics Developer Guide. To run each example, you need an Amazon Kinesis Analytics application that has the sample stock ticker input stream. To learn how to create an Analytics application and configure the sample stock ticker input stream, see Getting Started in the Amazon Kinesis Analytics Developer Guide.

The sample stock dataset has the schema following.



(ticker_symbol  VARCHAR(4),
sector          VARCHAR(16),
change          REAL,
price           REAL)

Example 1: Determine the statistical standard deviation of the values in a column in a tumbling window query

The following example demonstrates how to use the STDDEV_SAMP function to determine the standard deviation of the values in a tumbling window of the PRICE column of the example dataset. DISTINCT is not specified, so duplicate values are included in the calculation.


CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (ticker_symbol VARCHAR(4), stddev_samp_price REAL);

CREATE OR REPLACE  PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"

SELECT STREAM ticker_symbol, STDDEV_SAMP(price) AS stddev_samp_price
    FROM "SOURCE_SQL_STREAM_001"
    GROUP BY ticker_symbol, FLOOR(("SOURCE_SQL_STREAM_001".ROWTIME - TIMESTAMP '1970-01-01 00:00:00') SECOND / 10 TO SECOND);

Results

The preceding examples output a stream similar to the following:

Example 2: Determine the statistical standard deviation of the values in a columm in a sliding window query

The following example demonstrates how to use the STDDEV_SAMP function to determine the standard deviation of the values in a sliding window of the PRICE column of the example dataset. DISTINCT is not specified, so duplicate values are included in the calculation.


CREATE OR REPLACE STREAM "DESTINATION_SQL_STREAM" (ticker_symbol VARCHAR(4), stddev_samp_price REAL);

CREATE OR REPLACE PUMP "STREAM_PUMP" AS INSERT INTO "DESTINATION_SQL_STREAM"

SELECT STREAM ticker_symbol, STDDEV_SAMP(price) OVER TEN_SECOND_SLIDING_WINDOW AS stddev_samp_price
FROM "SOURCE_SQL_STREAM_001"
 
WINDOW TEN_SECOND_SLIDING_WINDOW AS (
  PARTITION BY ticker_symbol
  RANGE INTERVAL '10' SECOND PRECEDING);

The preceding example outputs a stream similar to the following: