Stream Settings

When using a Streaming method (HTTP Stream API, Kafka, AWS Kinesis) to ingest data, Hydrolix provides several customisations in how and when data is written into the table.

These customisations are provided as streaming event data often has the characteristics of containing "Hot" data that needs to be made available as soon as possible, and "cold" data that arrives late or out of order. A good example of this are CDN or appliance logs where 95% of logs are provided within a 15 minutes window with the last 5% being supplied later in a 24 hour period.

Settings for Hot and Cold data can be independently configured and cover a variety of "tunables" that determine how often event partitions are flushed to storage and how many partitions are being worked on at any one time.

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Defaults

It is recommended that before changing settings the defaults be used initially. The default settings have been developed to meet the majority of use cases.

Configuring Settings

The settings for how data is written to the table are configured either through the portal and under the sources menu or via the Restful API - Tables.

To effectively manage incoming stream data, Hydrolix Streaming has configurable options for Hot and Cold data.

  • Hot data is near-term data defined as an event received within the hot_data_max_age_minutes window from now().
  • Cold data is defined as data that is late arriving event data that is beyond the hot_data_max_age_minutes but before the cold_data_max_age_days.
  • Data beyond the cold_data_max_age_days, it is rejected and ignored.

Hot Settings

The following settings are provided to define Hot data and how it is written.

Element

Description

Default

hot_data_max_age_minutes

How long data is determined to be Hot from now + hot_data_max_age_minutes. Incoming events have their primary (datetime) column inspected and evaluated with older events than this considered to old to be "hot".

3

hot_data_max_active_partitions

The maximum number of partitions to keep open on the server at any one time.

3

hot_data_max_rows_per_partition

The maximum size (measured in number of rows) to allow any open partition to reach.

12288000

hot_data_max_minutes_per_partition

The maximum width in time of a partition. This is the maximum allowable distance between the newest and oldest primary of rows in the partition.

1

hot_data_max_open_seconds

The maximum duration (in wall clock time) to wait for events to trickle in for a recent-data partition.

60

hot_data_max_idle_seconds

The maximum duration to wait from the last received event before automatically closing an open partition.

30

Cold Settings

The following settings are provided to define Cold data and how it is written.

Element

Description

Default

cold_data_max_age_days

How long data is determined to be Cold from now + hot_data_max_age_minutes. Incoming events have the primary (datetime) column inspected and evaluated with older events than this considered too old to be worth indexing at all and will be consigned to the scrap heap of history (well Rejects ).

365

cold_data_max_active_partitions

The maximum number of partitions to keep open at any one time.

50

cold_data_max_rows_per_partition

The maximum size (measured in number of rows) to allow any open partition to reach.

12288000

cold_data_max_minutes_per_partition

The maximum width in time of a partition. This is the maximum allowable distance between the newest and oldest primary of rows in the partition.

60

cold_data_max_open_seconds

The maximum duration (in wall clock time) to wait for events to trickle in for a recent-data partition.

300

cold_data_max_idle_seconds

The maximum duration to wait for new data to appear at all before automatically closing an open partition.

60

Configuring via the API

To configure the table via the API the Tables API endpoints are used.

The following is an example:

POST 'http://myhost/config/v1/orgs/my_org_uuid/projects/my_project_uuid/tables/

{
      "project": "{{project_uuid}}",
      "name": "mytable",
      "description": "An example table",
      "settings": {
         "stream": {
            "hot_data_max_age_minutes": 15,
            "hot_data_max_active_partitions": 4,
            "hot_data_max_rows_per_partition": 1000000,
            "hot_data_max_minutes_per_partition": 5,
            "hot_data_max_open_seconds": 60,
            "hot_data_max_idle_seconds": 30,
            "cold_data_max_age_days": 365,
            "cold_data_max_active_partitions": 5,
            "cold_data_max_rows_per_partition": 1000000,
            "cold_data_max_minutes_per_partition": 15,
            "cold_data_max_open_seconds": 60,
            "cold_data_max_idle_seconds": 30
         }
      }
}

Interdependence with Merge.

More information on merge can be found here. It should be noted that the volume of partitions written directly by the Stream affects the merge service - i.e. more/less partitions to merge later.

Where a higher number of partitions are being created during the initial ingest stream it is important to ensure there are a sufficient amount of merge peers created to merge the resultant data. A higher partition count in the initial loading process may require higher counts (or size) of merge servers.


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