Windowing Reference

๐Ÿ“˜

Prior Knowledge

In this page, we'll assume that the reader is already familiar with how to use SQL's "group by" clause to aggregate data in a batch setting.

In batch processing, a SQL query that summarizes a table may require the server to read through every row in the table before returning a result. How is a streaming pipeline able to summarize a series of events that can continue forever? The answer is windowing. Windows create subsets of your streams with a start time and end time. A pipeline can then count or sum the windowed data and output the final totals for each time period.

These windows can be created based on either event time (when the event originally occurred) or processing time (when the pipeline reads the event). Event time is usually preferable. See the time in pipelines section of the pipelines documentation to understand why. To use event time, a watermark must be set on the input stream. See the watermarks section of the streams documentation for more information.

Table-Valued Functions (TVF)

Window aggregations in Decodable SQL look much like standard SQL aggregations. They use functions like count and sum in the select portion of the statement and then define how records should be grouped using the group by clause. The difference is that the source stream in the from clause is replaced with a table supplied by a Table-Valued Function:

insert into <output-stream>
select window_start, window_end, count(*) as count
from table(<table-valued-function>(...))
group by window_start, window_end

A TVF transforms a stream into time-bounded buckets of events that can be aggregated. It must be wrapped in the table() function when used in a pipeline's SQL. While Decodable doesn't support outputting the results of a TVF directly, you can think about them as a version of the input data with the additional fields window_start and window_end.

Decodable supports Table-Valued Functions that implement a number of windowing strategies including tumble, hop, and cumulate:

Tumble

Tumble creates adjoining windows of equal size. It counts each input event in exactly one window.

244244

tumble(table <data>, descriptor(<timecol>), <size>)

Argument

Description

data

your input stream name

timecol

the name of your watermarked event time field

size

a duration specifying the width of the windows

note: durations are specified using interval syntax (e.g. interval '5' seconds)

๐Ÿ‘

Use Case

A tumble window can be used to summarize customer orders by hour while preserving the total count of items ordered and the total sum of payments received.

-- Tumble use case SQL

insert into orders_hourly
select window_start, window_end, count(*) as orders, sum(payment) as payment
from table(
    tumble(table orders, descriptor(`timestamp`), interval '1' hours))
group by window_start, window_end

Hop

Hop creates windows of equal size which may overlap. For this reason, an input event may be present in more than one window. Hopping windows are also known as "sliding windows".

244244

hop(table <data>, descriptor(<timecol>), <slide>, <size>)

Argument

Description

data

your input stream name

timecol

the name of your watermarked event time field

slide

a duration specifying the time between the start of sequential hopping windows

size

a duration specifying the width of the windows

note: durations are specified using interval syntax (e.g. interval '5' seconds)

๐Ÿ‘

Use Case

Hopping windows can be used to create a summary of recent web requests from various IP address ranges.

-- Hop use case SQL

insert into recent_requests
select window_start, window_end, ip_range, count(*) as requests, sum(bytes) as bytes
from table(
    hop(table orders, descriptor(`timestamp`), interval '1' minutes, interval '15' minutes))
group by window_start, window_end, ip_range

Cumulate

Cumulate creates cumulative windows of increasing size from the same start time until a maximum window size is reached. At that point, the previous window is closed and a new one is opened. Input events may be counted in more than one window.

12561256

cumulate(table <data>, descriptor(<timecol>), <step>, <size>)

Argument

Description

data

your input stream name

timecol

the name of your watermarked event time field

step

a duration speficying the difference in window size between the end of sequential cumulating windows

size

a duration specifying the maximum size of the cumulating windows (must be an integer multiple of step)

note: durations are specified using interval syntax (e.g. interval '5' seconds)

๐Ÿ‘

Use Case

Cumulative windows can be used to provide an updated count of unique daily users every 5 minutes.

-- Cumulate use case SQL

insert into current_daily_active_users
select window_start, window_end, count(distinct user_id) as unique_users
from table(
    cumulate(table orders, descriptor(`timestamp`), interval '5' minutes, interval '1' day))
group by window_start, window_end

๐Ÿšง

Key Takeaways:

  • A window is a subset of your stream bounded by a start time and an end time.
  • To perform aggregation functions like count and sum, you must specify a window to perform the aggregation over using a Table-Valued Function (TVF).
  • To aggregate using event time, your input stream must have watermarks enabled. See Managing Streams for more on watermarks.
  • Decodable currently supports three windowing strategies:
    1. Tumble
    2. Hop
    3. Cumulate

Web UI Example

Here's an example showing how we can use windowing to aggregate logs of http activity using the tumble TVF:

19981998 20062006 20062006
-- SQL text box contents from screenshot provided here for your convenience
insert into http_aggregated
select window_start, window_end, original_path, sum(bytes_rcvd) as bytes_rcvd
from table(
    tumble(table http_parsed, descriptor(`timestamp`), interval '5' seconds))
group by window_start, window_end, original_path

CLI Example

Here is the same example using the Decodable CLI:

> decodable stream get cafb4d28
http_parsed
  id                       cafb4d28
  description              auto created output stream for pipeline [parse_http]
  schema
    0  timestamp           TIMESTAMP(3)
    1  method              STRING
    2  original_path       STRING
    3  response_code       INT
    4  bytes_rcvd          INT
  watermark                `timestamp` as `timestamp`
  create time              2022-06-08T18:36:20Z
  update time              2022-06-08T18:36:53Z
  
> decodable pipeline preview "select * from http_parsed"
Submitting query... done! (took 4.71s)
Waiting for results...
{"bytes_recv":8515,"method":"DELETE","original_path":"/products/2","response_code":201,"timestamp":"2022-06-07 22:24:16"}
{"bytes_recv":1399,"method":"GET","original_path":"/products/2","response_code":200,"timestamp":"2022-06-07 22:24:17"}
{"bytes_recv":384,"method":"PATCH","original_path":"/products/2","response_code":500,"timestamp":"2022-06-07 22:24:17"}
...
Records received:      10
Time to first record:  21.56s
Total time:            21.56so

> decodable pipeline preview "insert into http_aggregated
select window_start, window_end, original_path, sum(bytes_rcvd) as bytes_rcvd
from table(
    tumble(table http_parsed, descriptor(\`timestamp\`), interval '5' seconds))
group by window_start, window_end, original_path"
Submitting query... done! (took 7.51s)
Waiting for results...
{"bytes_rcvd":43233,"original_path":"/","window_end":"2022-06-09 20:17:30","window_start":"2022-06-09 20:17:25"}
{"bytes_rcvd":14714,"original_path":"/products/3","window_end":"2022-06-09 20:17:30","window_start":"2022-06-09 20:17:25"}
{"bytes_rcvd":15108,"original_path":"/users/1","window_end":"2022-06-09 20:17:30","window_start":"2022-06-09 20:17:25"}
{"bytes_rcvd":15938,"original_path":"/products/2","window_end":"2022-06-09 20:17:30","window_start":"2022-06-09 20:17:25"}
{"bytes_rcvd":56841,"original_path":"/products/3","window_end":"2022-06-09 20:17:35","window_start":"2022-06-09 20:17:30"}
{"bytes_rcvd":49969,"original_path":"/products/2","window_end":"2022-06-09 20:17:35","window_start":"2022-06-09 20:17:30"}
{"bytes_rcvd":48778,"original_path":"/","window_end":"2022-06-09 20:17:35","window_start":"2022-06-09 20:17:30"}
{"bytes_rcvd":54100,"original_path":"/users/1","window_end":"2022-06-09 20:17:35","window_start":"2022-06-09 20:17:30"}
{"bytes_rcvd":71614,"original_path":"/products/2","window_end":"2022-06-09 20:17:40","window_start":"2022-06-09 20:17:35"}
{"bytes_rcvd":53903,"original_path":"/","window_end":"2022-06-09 20:17:40","window_start":"2022-06-09 20:17:35"}
Records received:      10
Time to first record:  28.74s
Total time:            36.87s

๐Ÿ“˜

This page contains copied contents from the Apache Flinkยฎ documentation.

See Credits page for details.


Did this page help you?