InsertRollingFieldTimestampHeaders

Inserts the datetime as a message header from a value field and a rolling window configuration.

A Kafka Connect Single Message Transform (SMT) that inserts date, year, month,day, hour, minute and second headers using a timestamp field from the record payload and a rolling time window configuration. The timestamp field can be in various valid formats, including long integers, strings, or date objects. The timestamp field can originate from either the record Key or the record Value. When extracting from the record Key, prefix the field with _key.; otherwise, extract from the record Value by default or explicitly using the field without prefixing. For string-formatted fields, specify a format.from.pattern parameter to define the parsing pattern. Long integer fields are assumed to be Unix timestamps; the desired Unix precision can be specified using the unix.precision parameter.

The headers inserted are of type STRING. By using this SMT, you can partition the data by yyyy-MM-dd/HH or yyyy/MM/dd/HH, for example, and only use one SMT.

The list of headers inserted are:

• date

• year

• month

• day

• hour

• minute

• second

All headers can be prefixed with a custom prefix. For example, if the prefix is wallclock_, then the headers will be:

• wallclock_date

• wallclock_year

• wallclock_month

• wallclock_day

• wallclock_hour

• wallclock_minute

• wallclock_second

When used with the Lenses connectors for S3, GCS or Azure data lake, the headers can be used to partition the data. Considering the headers have been prefixed by _, here are a few KCQL examples:

connect.s3.kcql=INSERT INTO $bucket:prefix SELECT * FROM kafka_topic PARTITIONBY _header._date, _header._hour
connect.s3.kcql=INSERT INTO $bucket:prefix SELECT * FROM kafka_topic PARTITIONBY _header._year, _header._month, _header._day, _header._hour

Transform Type Class

io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders

Configuration

Name	Description	Type	Default
field	The field name. If the key is part of the record Key, prefix with _key; otherwise _value. If _value or _key is not used, it defaults to the record Value to resolve the field.	String
format.from.pattern	Optional DateTimeFormatter-compatible format for the timestamp. Used to parse the input if the input is a string. Multiple (fallback) patterns can be added, comma-separated.	String
unix.precision	Optional. The desired Unix precision for the timestamp: seconds, milliseconds, microseconds, or nanoseconds. Used to parse the input if the input is a Long.	String	milliseconds
header.prefix.name	Optional header prefix.	String
date.format	Optional Java date time formatter.	String	yyyy-MM-dd
year.format	Optional Java date time formatter for the year component.	String	yyyy
month.format	Optional Java date time formatter for the month component.	String	MM
day.format	Optional Java date time formatter for the day component.	String	dd
hour.format	Optional Java date time formatter for the hour component.	String	HH
minute.format	Optional Java date time formatter for the minute component.	String	mm
second.format	Optional Java date time formatter for the second component.	String	ss
timezone	Optional. Sets the timezone. It can be any valid Java timezone.	String	UTC
locale	Optional. Sets the locale. It can be any valid Java locale.	String	en
rolling.window.type	Sets the window type. It can be fixed or rolling.	String	minutes
rolling.window.size	Sets the window size. It can be any positive integer, and depending on the window.type it has an upper bound, 60 for seconds and minutes, and 24 for hours.	Int	15
## Example

To store the epoch value, use the following configuration:

transforms=rollingWindow
transforms.rollingWindow.type=io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders
transforms.rollingWindow.field=created_at
transforms.rollingWindow.rolling.window.type=minutes
transforms.rollingWindow.rolling.window.size=15

To prefix the headers with wallclock_, use the following:

transforms=rollingWindow
transforms.rollingWindow.type=io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders
transforms.rollingWindow.field=created_at
transforms.rollingWindow.header.prefix.name=wallclock_
transforms.rollingWindow.rolling.window.type=minutes
transforms.rollingWindow.rolling.window.size=15

To change the date format, use the following:

transforms=rollingWindow
transforms.rollingWindow.type=io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders
transforms.rollingWindow.field=created_at
transforms.rollingWindow.header.prefix.name=wallclock_
transforms.rollingWindow.rolling.window.type=minutes
transforms.rollingWindow.rolling.window.size=15
transforms.rollingWindow.date.format="date=yyyy-MM-dd"

To use the timezone Asia/Kolkoata, use the following:

transforms=rollingWindow
transforms.rollingWindow.type=io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders
transforms.rollingWindow.field=created_at
transforms.rollingWindow.header.prefix.name=wallclock_
transforms.rollingWindow.rolling.window.type=minutes
transforms.rollingWindow.rolling.window.size=15
transforms.rollingWindow.timezone=Asia/Kolkata

To facilitate S3, GCS, or Azure Data Lake partitioning using a Hive-like partition name format, such as date=yyyy-MM-dd / hour=HH, employ the following SMT configuration for a partition strategy.

transforms=rollingWindow
transforms.rollingWindow.type=io.lenses.connect.smt.header.InsertRollingFieldTimestampHeaders
transforms.rollingWindow.field=created_at
transforms.rollingWindow.rolling.window.type=minutes
transforms.rollingWindow.rolling.window.size=15
transforms.rollingWindow.timezone=Asia/Kolkata
transforms.rollingWindow.date.format="date=yyyy-MM-dd"
transforms.rollingWindow.hour.format="hour=yyyy"

and in the KCQL setting utilise the headers as partitioning keys:

connect.s3.kcql=INSERT INTO $bucket:prefix SELECT * FROM kafka_topic PARTITIONBY _header.date, _header.year

Configuration for format.from.pattern

Configuring multiple format.from.pattern items requires careful thought as to ordering and may indicate that your Kafka topics or data processing techniques are not aligning with best practices. Ideally, each topic should have a single, consistent message format to ensure data integrity and simplify processing.

Multiple Patterns Support

The format.from.pattern field supports multiple DateTimeFormatter patterns in a comma-separated list to handle various timestamp formats. Patterns containing commas should be enclosed in double quotes. For example:

format.from.pattern=yyyyMMddHHmmssSSS,"yyyy-MM-dd'T'HH:mm:ss,SSS"

Best Practices

While this flexibility can be useful, it is generally not recommended due to potential complexity and inconsistency. Ideally, a topic should have a single message format to align with Kafka best practices, ensuring consistency and simplifying data processing.

Configuration Order

The order of patterns in format.from.pattern matters. Less granular formats should follow more specific ones to avoid data loss. For example, place yyyy-MM-dd after yyyy-MM-dd'T'HH:mm:ss to ensure detailed timestamp information is preserved.