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Time and Windows in KSQL¶
In KSQL, a record is an immutable representation of an event in time. Each record carries a timestamp, which determines its position on the time axis.
This is the default timestamp that KSQL uses for processing the record. The timestamp is set either by the producer application or by the Apache Kafka® broker, depending on the topic’s configuration. Records may be out-of-order within the stream.
Timestamps are used by time-dependent operations, like aggregations and joins.
Timestamps have different meanings, depending on the implementation. A record’s timestamp can refer to the time when the event occurred, or when the record was ingested into Kafka, or when the record was processed. These times are event-time, ingestion-time, and processing-time.
The time when a record is created by the data source. Achieving event-time semantics requires embedding timestamps in records when an event occurs and the record is produced.
For example, if the record is a geo-location change reported by a GPS sensor in a car, the associated event-time is the time when the GPS sensor captured the location change.
The time when a record is stored in a topic partition by a Kafka broker. Ingestion-time is similar to event-time, as a timestamp is embedded in the record, but the ingestion timestamp is generated when the Kafka broker appends the record to the target topic.
Ingestion-time can approximate event-time if the time difference between the creation of the record and its ingestion into Kafka is small.
For use cases where event-time semantics aren’t possible, ingestion-time may be an alternative. Consider using ingestion-time when data producers don’t embed timestamps in records, as in older versions of Kafka’s Java producer client, or when the producer can’t assign timestamps directly, like when it doesn’t have access to a local clock.
The time when the record is consumed by a stream processing application. The processing-time can occur immediately after ingestion-time, or it may be delayed by milliseconds, hours, or days.
For example, imagine an analytics application that reads and processes the geo-location data reported from car sensors, and presents it to a fleet-management dashboard. In this case, processing-time in the analytics application might be many minutes or hours after the event-time, as cars can move out of mobile reception for periods of time and have to buffer records locally.
Don’t mix streams or tables that have different time semantics.
A record’s timestamp is set either by the record’s producer or by the Kafka
broker, depending on the topic’s timestamp configuration. The topic’s
setting can be either
- The broker uses the the record’s timestamp as set by the producer. This setting enforces event-time semantics.
- The broker overwrites the record’s timestamp with the broker’s local time
when it appends the record to the topic’s log. This setting enforces
ingestion-time semantics. If
LogAppendTimeis configured, the producer has no control over the timestamp.
KSQL doesn’t support processing-time operations directly, but you can implement user-defined functions (UDFs) that access the current time. For more information, see KSQL Custom Function Reference (UDF and UDAF).
By default, when KSQL imports a topic to create a stream, it uses the record’s timestamp, but you can add the WITH(TIMESTAMP=’some-field’) clause to use a different field from the record’s value as the timestamp. The optional TIMESTAMP_FORMAT property indicates how KSQL should parse the field. The field you specify can be an event-time or an ingestion-time. This approach implements payload-time semantics.
If you use the WITH(TIMESTAMP=…) clause, this timestamp must be expressible as a Unix epoch time in milliseconds, which is the number of milliseconds that have elapsed since 1 January 1970 at midnight UTC/GMT. Also, you can specify the timestamp as a string when you provide a TIMESTAMP_FORMAT. For more information, see KSQL Timestamp Formats.
When working with time you should also make sure that additional aspects of time, like time zones and calendars, are correctly synchronized – or at least understood and traced – throughout your streaming data pipelines. It helps to agree on specifying time information in UTC or in Unix time, like seconds since the Unix epoch, everywhere in your system.
Timestamps of KSQL Output Streams¶
When a KSQL application writes new records to Kafka, it assigns timestamps to the records it creates. Timestamps are assigned based on context:
- When new output records are generated by processing an input record directly, output record timestamps are inherited from input record timestamps.
- When new output records are generated by a periodic function, the output record timestamp is defined as the current internal time of the stream task.
- For aggregations, the timestamp of the resulting update record is taken from the latest input record that triggered the update.
Producers and Timestamps¶
A producer application can set the timestamp on its records to any value, but usually, it choses a sensible event-time or the current wall-clock time.
If the topic’s message.timestamp.type
configuration is set to
CreateTime, the following holds for the producer:
- When a producer record is created, it contains no timestamp, by default.
- The producer can set the timestamp on the record explicitly.
- If the timestamp isn’t set when the producer application calls the
producer.send()method, the current wall-clock time is set automatically.
In all three cases, the time semantics are considered to be event-time.
When KSQL imports a topic to create a stream, it gets the timestamp from the topic’s messages by using a timestamp extractor class. Timestamp extractors implement the TimestampExtractor interface.
Concrete implementations of timestamp extractors may retrieve or compute timestamps based on the actual contents of data records, like an embedded timestamp field, to provide event-time or ingestion-time semantics, or they may use any other approach, like returning the current wall-clock time at the time of processing to implement processing-time semantics.
By creating a custom timestamp extractor class, you can enforce different notions or semantics of time, depending on the requirements of your business logic. For more information see default.timestamp.extractor.
Windows in KSQL Queries¶
Representing time consistently enables aggregation operations on streams and tables, like SUM, that have distinct time boundaries. In KSQL, these boundaries are named windows.
A window has a start time and an end time, which you access in your queries by using the WINDOWSTART() and WINDOWEND() functions.
Windowing lets you control how to group records that have the same key for stateful operations, like aggregations or joins, into time spans. KSQL tracks windows per record key.
A related operation is grouping, which groups all records that have the same key to ensure that records are properly partitioned, or “keyed”, for subsequent operations. When you use the GROUP BY clause in a query, windowing enables you to further sub-group the records of a key.
When using windows in your KSQL queries, aggregate functions are applied only to the records that occur within a specific time window. Records that arrive late are handled as you might expect: although the time window they belong to has expired, the late records are still associated with the correct window.
You can specify a retention period for the window in your KSQL queries. This retention period controls how long KSQL waits for out-of-order or late-arriving records for a given window. If a record arrives after the retention period of a window has passed, the record is discarded and isn’t processed in that window.
In the real world, late-arriving records are always possible, and your KSQL applications must account for them properly. The system’s time semantics determine how late records are handled. For processing-time, the semantics are “when the record is being processed”, which means that the notion of late records isn’t applicable because, by definition, no record can be late.
Late-arriving records are considered “late” only for event-time and ingestion-time semantics. In both cases, KSQL is able to handle late-arriving records properly.
KSQL is based on the Unix epoch time in the UTC timezone, and this can affect time windows. For example, if you define a 24-hour tumbling time window, it will be in the UTC timezone, which may not be appropriate if you want to have daily windows in your timezone.
There are three ways to define time windows in KSQL: hopping windows, tumbling windows, and session windows. Hopping and tumbling windows are time windows, because they’re defined by fixed durations they you specify. Session windows are dynamically sized based on incoming data and defined by periods of activity separated by gaps of inactivity.
|Hopping Window||Time-based||Fixed-duration, overlapping windows|
|Tumbling Window||Time-based||Fixed-duration, non-overlapping, gap-less windows|
|Session Window||Session-based||Dynamically-sized, non-overlapping, data-driven windows|
Hopping windows are based on time intervals. They model fixed-sized, possibly overlapping windows. A hopping window is defined by two properties: the window’s duration and its advance, or “hop”, interval. The advance interval specifies how far a window moves forward in time relative to the previous window. For example, you can configure a hopping window with a duration of five minutes and an advance interval of one minute. Because hopping windows can overlap, and usually they do, a record can belong to more than one such window.
All hopping windows have the same duration, but they might overlap, depending on the length of time specified in the ADVANCE BY property.
For example, if you want to count the pageviews for only
Region_6 by female
users for a hopping window of 30 seconds that advances by 10 seconds, you might
run a query like this:
SELECT regionid, COUNT(*) FROM pageviews \ WINDOW HOPPING (SIZE 30 SECONDS, ADVANCE BY 10 SECONDS) \ WHERE UCASE(gender)='FEMALE' AND LCASE (regionid) LIKE '%_6' \ GROUP BY regionid;
The hopping window’s start time is inclusive, but the end time is exclusive. This is important for non-overlapping windows, in which each record must be contained in exactly one window.
Tumbling windows are a special case of hopping windows. Like hopping windows, tumbling windows are based on time intervals. They model fixed-size, non-overlapping, gap-less windows. A tumbling window is defined by a single property: the window’s duration. A tumbling window is a hopping window whose window duration is equal to its advance interval. Since tumbling windows never overlap, a record will belong to one and only one window.
All tumbling windows are the same size and adjacent to each other, which means that whenever a window ends, the next window starts.
For example, if you want to compute the the five highest-value orders
per zip code per hour in an
orders stream, you might run a query like this:
SELECT orderzip_code, TOPK(order_total, 5) FROM orders \ WINDOW TUMBLING (SIZE 1 HOUR) GROUP BY order_zipcode;
Here’s another example: to detect potential credit card fraud in an
authorization_attempts stream, you might run a query for the number of
authorization attempts on a particular card that’s greater than three, during
a time interval of five seconds.
SELECT card_number, count(*) FROM authorization_attempts \ WINDOW TUMBLING (SIZE 5 SECONDS) \ GROUP BY card_number HAVING COUNT(*) > 3;
The tumbling window’s start time is inclusive, but the end time is exclusive. This is important for non-overlapping windows, in which each record must be contained in exactly one window.
A session window aggregates records into a session, which represents a period of activity separated by a specified gap of inactivity, or “idleness”. Any records with timestamps that occur within the inactivity gap of existing sessions are merged into the existing sessions. If a record’s timestamp occurs outside of the session gap, a new session is created.
A new session window starts if the last record that arrived is further back in time than the specified inactivity gap.
Session windows are different from the other window types, because:
- KSQL tracks all session windows independently across keys, so windows of different keys typically have different start and end times.
- Session window durations vary. Even windows for the same key typically have different durations.
Session windows are especially useful for user behavior analysis. Session-based analyses range from simple metrics, like counting user visits on a news website or social platform, to more complex metrics, like customer-conversion funnel and event flows.
For example, to count the number of pageviews per region for session windows with a session inactivity gap of 60 seconds, you might run the following query, which “sessionizes” the input data and performs the counting/aggregation step per region:
SELECT regionid, COUNT(*) FROM pageviews \ WINDOW SESSION (60 SECONDS) \ GROUP BY regionid;
The start and end times for a session window are both inclusive, in contrast to time windows.
A session window contains at least one record. It’s not possible for a session window to have zero records.
If a session window contains exactly one record, the record’s ROWTIME timestamp is identical to the window’s own start and end times. Access these by using the WINDOWSTART() and WINDOWEND() functions.
If a session window contains two or more records, then the earliest/oldest record’s ROWTIME timestamp is identical to the window’s start time, and the latest/newest record’s ROWTIME timestamp is identical to the window’s end time.
KSQL supports using windows in JOIN queries by using the WITHIN clause.
For example, to find orders that have shipped within the last hour from an
orders stream and a
shipments stream, you might run a query like:
SELECT o.order_id, o.total_amount, o.customer_name, s.shipment_id, s.warehouse \ FROM new_orders o \ INNER JOIN shipments s \ WITHIN 1 HOURS \ ON o.order_id = s.order_id;
For more information on joins, see Join Event Streams with KSQL.
- Create a KSQL Stream
- Writing Streaming Queries Against Apache Kafka® Using KSQL (Docker)
- Stream Processing Cookbook: Event Time Processing
- Stream Processing Cookbook: Detecting and Analyzing Suspicious Network Activity
- For a realistic example that manipulates timestamps and uses windows in KSQL queries, see KSQL in Action: Real-Time Streaming ETL from Oracle Transactional Data.