Timeplus Enterprise v2 and Proton

This page summarizes changes for each major update in Timeplus Enterprise v2 and Timeplus Proton, including new features and important bug fixes.

Jun 24, 2024

Timeplus Enterprise v2 is now available for cloud or self-hosted deployment. See installation options for self-hosting.

When creating an external stream for Apache Kafka and running select count() from table(stream), Timeplus Proton will retrieve the number of messages in the Kafka topic. If you set data_format = "RawBLOB", one_message_per_row=false, Proton will use the Kafka offset to get the message count. But if one_message_per_row=true, then Proton will read each message to split the rows. It won’t retrieve the result as fast as calculating the offset, but it will be more accurate.
When using an external stream to write data to Kafka, a new setting kafka_max_message_size is now available. When the external stream is created with data_format='JSONEachRow',one_message_per_row=false, each message in Kafka may contain multiple rows, with each row being a JSON document. This new setting kafka_max_message_size will control how much data will be put in a Kafka message and when to create a new Kafka message, ensuring each message won't exceed the kafka_max_message_size limit.
New function: array_fold, which is equivalent to the Array.reduce function in JavaScript, and is used to fold or reduce the elements in an array from left to right by applying a lambda-function to the array elements in a cumulative manner.
array_max/min functions now support datetime.

Self-Hosted:

Account setup flow for self-hosted deployment: create account and login screens.
License management: In Workspace Settings, see your existing licenses and entitlements. Contact us to purchase a new license, then enter the license key and upload the license file in Workspace Settings.
Usage monitoring: Also in Workspace Settings, see your daily usage for ingested data and scanned external stream data.
Preview: Python User-Defined Functions (UDF). Create UDFs by leveraging Python libraries, such as pandas. Contact us to try this feature.