Batch Ingestion - Jobs

Batch ingestion is used to ingest multiple files at once into Hydrolix. To use batch ingestion, you must:

1. Create a table
2. Create a transform
3. Submit a batch job request via the UI or API

You can submit multiple batch jobs for the same table as required.

Note: make sure the S3 bucket has been added to --bucket-allowlist by your Hydrolix administrator.

{
	"type": "batch_import",
	"name": "job sample trips",
	"description": "sample_trips batchjob",
	"settings": {
		"max_rows_per_partition": 10000000,
		"max_minutes_per_partition": 14400,
		"source": {
			"table": "sample.trips",
			"type": "batch",
			"subtype": "aws s3",
			"transform": "transform_trips",
			"settings": {
				"url": "s3://mydatatoingest"
			}
		}
	}
}

Example ingest use cases based on url path

• A single file i.e. "s3://mybucket/another/file.gz"
• All files in a single bucket i.e. "s3://mybucket/another/"
• All files matching regex pattern i.e. "s3://mybucket/" along with "settings.regex_filter": "^s3://mybucket/.*/.*.gz"

Job Attributes

A transform describes the shape of individual pieces of data. A job describes how to treat the data set as a whole as it is being ingested.

settings

{ ...
"settings": {
		"max_rows_per_partition": 10000000,
		"max_minutes_per_partition": 14400,
		"source": {
			...
		}
	}
}

Some data sets consist of many small files, other data sets consist of fewer larger files. Hydrolix ultimately writes data into “partitions”. The number and size of partitions influences performance of query.

What is best for each data set is an “it depends” answer, however, consider:

1. Partitions are a single unit to be processed. This means that queries of large partitions cannot be parallelized as much as smaller partitions.
2. Smaller partitions mean more parallelization, but also mean less efficient use of resources.

Regex Filter.

If data is stored in a complex bucket structure on AWS S3 and cannot be expressed with a simple S3 path. regex_filter allows you to express the structure pattern to search. It is used in conjuction with settings.url which narrows down the scope.

Given the following example s3 source path s3://mybucket/level1/2020/01/app1/pattern_xyz.gz with setting "url":"s3://mybucket/".

Possible regex_filter pattern could be:

• ^.*\\.gz$
• ^s3://mybucket/level1/2020/\\d{2}/app1/.*.gz
• ^.*/level1/2020/\\d{2}/app1/.*.gz
• ^.*/level1/2020/\\d{2}/.*/pattern_\\w{3}.gz
• ^.*/level1/2020/\\d{2}/.*/pattern_.*.gz

Tuning Input Jobs

For tuning the partition size based on the input data files, We provide 3 parameters:

Ingest Parallelization

Batch ingest is performed on compute instances. Batch performance can be improved by:

1. Adding more batch instances
2. Adding larger batch instances with more parallelism

Each scenario has the potential to be different. The type and number of instances can be adjusted via Hydrolix configuration. max_active_partitions tells Hydrolix how many partitions it should work on in parallel at one time.

max_active_partitionstotal number of partitions that should be processing on a single batch peer at a time - this is a balance of speed and memory

source

{
    ...
        "source": {
					"table": "sample.trips",
					"type": "batch",
					"subtype": "aws s3",
					"transform": "transform_trips",
					"settings": {
						"url": "s3://mydatatoingest"
					}
			}
    ...
}

The source element specifies information about the data itself, where it is, and how it should be treated.

Cancel Jobs

Use the cancel jobs endpoint to cancel the batch ingest job and tasks associated with the job ID. The cancellation will be reflected in the status output.

Response codes

Jobs Status

Get the status of a job and it’s tasks. This endpoint is suitable for polling for job completion.

Response codes

Response body on success

{
  "status": "RUNNING",
  "status_detail": {
    "tasks": {
      "INDEX": {
        "READY": 5
      },
      "LIST": {
        "DONE": 1
      }
    },
    "percent_complete": 0.16666667,
    "estimated": false
  }
}