Batch ingestion loads data from a storage bucket into a target table. We provide the following mechanisms:

• Batch Job API. A one-off task that will load one or more files based on the Job configuration and then stop.
• Batch auto-ingest. A continuous task of ingesting new files arriving in a storage bucket, through a combination of table settings, cloud provider pub/sub and cloud storage notification mechanism.

Batch ingest supports CSV and JSON data formats. Hydrolix requires read permissions to access external storage buckets.

The majority of the work is done by the batch-peer and batch-indexer containers in a pod called batch-peer. By default, batch ingest processes one file at a time, with the replicas tunable set to a value of 1. Increase the value of the replicas tunable to increase parallelism. The containers use a predefined resource profile, but you can override memory or storage settings if needed. For more details, see Scale your Kubernetes Cluster.

As of Hydrolix v4.14, if you have summary tables using data from the tables in which you load data, those summary tables will also be populated.

You can configure cloud storage to notify Hydrolix when new data is available for ingest. For more information, see the following pages:

• AWS S3 Notify.
• GCP Storage Notify.

Create a Batch Ingest Job via API

👍

Prerequisite Steps

• Create Project providing a name and description
• Create Table providing a name and ingest settings
• Create a Transform (Write Schema)

Once you've completed the prerequisites, Create a Batch Job. You must specify the following:

• the table where Hydrolix will store the data
• the transform Hydrolix should use to process the data
• the URL where Hydrolix should fetch the data
• the regex filter Hydrolix should use to limit ingestion to a subset of data (optional)

AWS Example

{
    "type": "batch_import",
    "name": "job_sample_data",
    "description": "sample data on aws",
    "settings": {
        "max_active_partitions": 576,
        "max_rows_per_partition": 33554432,
        "max_minutes_per_partition": 20,
        "source": {
            "settings": {
                "url": "s3://mydatatoingest/mypath/"
            },
            "table": "sample.data",
            "type": "batch",
            "subtype": "aws s3",
            "transform": "mytransform"
        },
        "regex_filter": "^s3://mydatatoingest/mypath/.*.gz"
    }
}

GCP Example

🚧

GCP/GKE Note;

Make sure to add the bucket permissions to your service account. For example:

gsutil iam ch serviceAccount:${GCP_STORAGE_SA}:roles/storage.objectAdmin gs://my bucket

{
    "type": "batch_import",
    "name": "job_sample_data",
    "description": "sample data on gcp",
    "settings": {
        "max_active_partitions": 576,
        "max_rows_per_partition": 33554432,
        "max_minutes_per_partition": 20,
        "source": {
            "settings": {
                "url": "gs://mydatatoingest/mypath/"
            },
            "table": "sample.data",
            "type": "batch",
            "subtype": "gcp gs",
            "transform": "mytransform"
        },
        "regex_filter": "^gs://burninbucket/gcp-prod-test/.*.gz"
    }
}

Linode Example

🚧

Limitations

The k8s cluster must use the same account as the Linode bucket. Linode storage does not support auto-ingest.

{
    "type": "batch_import",
    "name": "job_sample_data",
    "description": "sample data on Linode",
    "settings": {
        "max_active_partitions": 576,
        "max_rows_per_partition": 33554432,
        "max_minutes_per_partition": 20,
        "source": {
            "settings": {
                "url": "s3://mydatatoingest/mypath/"
            },
            "table": "sample.data",
            "type": "batch",
            "subtype": "aws s3",
            "transform": "mytransform"
        },
        "regex_filter": "^s3://mydatatoingest/mypath/.*.gz"
    }
}

Job Attributes

A job describes how to treat the data set as a whole as it is being ingested. Hydrolix batch jobs can employ varying file and path structures to load data. A single file, directory of files and a directory of files with a filter can all be applied.

For example:

• 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"

The settings object

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.

Example Settings:

{ ...
"settings": {
  	"max_active_partitions": 576,
		"max_rows_per_partition": 10000000,
		"max_minutes_per_partition": 14400,
    "input_concurrency": 1,
    "input_aggregation": 1536000000,
    "max_files": 0,
    "dry_run": false,
		"source": {
			...
		}
	}
}

The following are the default settings. We would suggest starting with the defaults and then tuning.

A note on 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

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_.*.gz576

The source element

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

Example source:

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

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 /v1/orgs/{org_id}/jobs/batch/{job_id}/cancel.

Jobs Status

Get the status of a job and it's tasks. This endpoint is suitable for polling for job completion /v1/orgs/{org_id}/jobs/batch/{job_id}/status.

Job Response codes

Response body on success

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

AWS Data to GKE - Cross cloud

If you have data in AWS storage and your cluster is in Google GKE it is possible to load data from AWS.
To do this you will need to set-up a user and role within AWS that has access to the bucket you want to retrieve the data from.

You will then need to add the AWS Secret Key and ID to your Kubernetes deployment.

This can be done either through the use of the hkt command to create your hydrolixcluster.yaml and apply it as follows:

./hkt hydrolix-cluster --env AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID --env AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY > hydrolixcluster.yaml

or it can be done directly in the hydrolixcluster.yaml

spec:
  admin_email: .....
  ....
  env:
    AWS_ACCESS_KEY_ID: AWS_ACCESS_KEY_ID_HERE
    AWS_SECRET_ACCESS_KEY: AWS_ACCESS_SECRET_KEY_HERE
  host: ..........
  ip_allowlist:
  - source: ................

To run the job within the Batch Jobs API you will need to specify the URL path with an S3:// path and a Subtype of aws s3

{
    "type": "batch_import",
    "name": "job_sample_data",
    "description": "sample data on aws",
    "settings": {
        "max_active_partitions": 576,
        "max_rows_per_partition": 33554432,
        "max_minutes_per_partition": 20,
        "source": {
            "settings": {
                "url": "s3://mydatatoingest/mypath/"
            },
            "table": "sample.data",
            "type": "batch",
            "subtype": "aws s3",
            "transform": "mytransform"
        },
        "regex_filter": "^s3://mydatatoingest/mypath/.*.gz"
    }
}