Loading streaming data into Amazon OpenSearch Service

You can use OpenSearch Ingestion to directly load streaming data into your Amazon OpenSearch Service domain, without needing to use third-party solutions. To send data to OpenSearch Ingestion, you configure your data producers and the service automatically delivers the data to the domain or collection that you specify. To get started with OpenSearch Ingestion, see Tutorial: Ingesting data into a collection using Amazon OpenSearch Ingestion.

You can still use other sources to load streaming data, such as Amazon Data Firehose and Amazon CloudWatch Logs, which have built-in support for OpenSearch Service. Others, like Amazon S3, Amazon Kinesis Data Streams, and Amazon DynamoDB, use Amazon Lambda functions as event handlers. The Lambda functions respond to new data by processing it and streaming it to your domain.

Loading streaming data from OpenSearch Ingestion

You can use Amazon OpenSearch Ingestion to load data into an OpenSearch Service domain. You configure your data producers to send data to OpenSearch Ingestion, and it automatically delivers the data to the collection that you specify. You can also configure OpenSearch Ingestion to transform your data before delivering it. For more information, see Amazon OpenSearch Ingestion.

Loading streaming data from Amazon S3

You can use Lambda to send data to your OpenSearch Service domain from Amazon S3. New data that arrives in an S3 bucket triggers an event notification to Lambda, which then runs your custom code to perform the indexing.

This method of streaming data is extremely flexible. You can index object metadata, or if the object is plaintext, parse and index some elements of the object body. This section includes some unsophisticated Python sample code that uses regular expressions to parse a log file and index the matches.

Prerequisites

Before proceeding, you must have the following resources.

Create the Lambda deployment package

Deployment packages are ZIP or JAR files that contain your code and its dependencies. This section includes Python sample code. For other programming languages, see Lambda deployment packages in the Amazon Lambda Developer Guide.

Create the Lambda function

After you create the deployment package, you can create the Lambda function. When you create a function, choose a name, runtime (for example, Python 3.8), and IAM role. The IAM role defines the permissions for your function. For detailed instructions, see Create a Lambda function with the console in the Amazon Lambda Developer Guide.

This example assumes you're using the console. Choose Python 3.9 and a role that has S3 read permissions and OpenSearch Service write permissions, as shown in the following screenshot:

After you create the function, you must add a trigger. For this example, we want the code to run whenever a log file arrives in an S3 bucket:

Finally, you can upload your deployment package:

At this point, you have a complete set of resources: a bucket for log files, a function that runs whenever a log file is added to the bucket, code that performs the parsing and indexing, and an OpenSearch Service domain for searching and visualization.

Testing the Lambda Function

After you create the function, you can test it by uploading a file to the Amazon S3 bucket. Create a file named sample.log using following sample log lines:

12.345.678.90 - [10/Oct/2000:13:55:36 -0700] "PUT /some-file.jpg"
12.345.678.91 - [10/Oct/2000:14:56:14 -0700] "GET /some-file.jpg"

Upload the file to the logs folder of your S3 bucket. For instructions, see Upload an object to your bucket in the Amazon Simple Storage Service User Guide.

Then use the OpenSearch Service console or OpenSearch Dashboards to verify that the lambda-s3-index index contains two documents. You can also make a standard search request:

GET https://domain-name/lambda-s3-index/_search?pretty
{
  "hits" : {
    "total" : 2,
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "lambda-s3-index",
        "_type" : "_doc",
        "_id" : "vTYXaWIBJWV_TTkEuSDg",
        "_score" : 1.0,
        "_source" : {
          "ip" : "12.345.678.91",
          "message" : "GET /some-file.jpg",
          "timestamp" : "10/Oct/2000:14:56:14 -0700"
        }
      },
      {
        "_index" : "lambda-s3-index",
        "_type" : "_doc",
        "_id" : "vjYmaWIBJWV_TTkEuCAB",
        "_score" : 1.0,
        "_source" : {
          "ip" : "12.345.678.90",
          "message" : "PUT /some-file.jpg",
          "timestamp" : "10/Oct/2000:13:55:36 -0700"
        }
      }
    ]
  }
}

Loading streaming data from Amazon Kinesis Data Streams

You can load streaming data from Kinesis Data Streams to OpenSearch Service. New data that arrives in the data stream triggers an event notification to Lambda, which then runs your custom code to perform the indexing. This section includes some unsophisticated Python sample code.

Prerequisites

Before proceeding, you must have the following resources.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "es:ESHttpPost",
        "es:ESHttpPut",
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents",
        "kinesis:GetShardIterator",
        "kinesis:GetRecords",
        "kinesis:DescribeStream",
        "kinesis:ListStreams"
      ],
      "Resource": "*"
    }
  ]
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Create the Lambda function

Follow the instructions in Create the Lambda deployment package, but create a directory named kinesis-to-opensearch and use the following code for sample.py:

import base64
import boto3
import json
import requests
from requests_aws4auth import AWS4Auth

region = '' # e.g. us-west-1
service = 'es'
credentials = boto3.Session().get_credentials()
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)

host = '' # the OpenSearch Service domain, e.g. https://search-mydomain.us-west-1.es.amazonaws.com
index = 'lambda-kine-index'
datatype = '_doc'
url = host + '/' + index + '/' + datatype + '/'

headers = { "Content-Type": "application/json" }

def handler(event, context):
    count = 0
    for record in event['Records']:
        id = record['eventID']
        timestamp = record['kinesis']['approximateArrivalTimestamp']

        # Kinesis data is base64-encoded, so decode here
        message = base64.b64decode(record['kinesis']['data'])

        # Create the JSON document
        document = { "id": id, "timestamp": timestamp, "message": message }
        # Index the document
        r = requests.put(url + id, auth=awsauth, json=document, headers=headers)
        count += 1
    return 'Processed ' + str(count) + ' items.'

Edit the variables for region and host.

Install pip if you haven't already, then use the following commands to install your dependencies:

cd kinesis-to-opensearch

pip install --target ./package requests
pip install --target ./package requests_aws4auth

Then follow the instructions in Create the Lambda function, but specify the IAM role from Prerequisites and the following settings for the trigger:

To learn more, see What is Amazon Kinesis Data Streams? in the Amazon Kinesis Data Streams Developer Guide.

At this point, you have a complete set of resources: a Kinesis data stream, a function that runs after the stream receives new data and indexes that data, and an OpenSearch Service domain for searching and visualization.

Test the Lambda Function

After you create the function, you can test it by adding a new record to the data stream using the Amazon CLI:

aws kinesis put-record --stream-name test --data "My test data." --partition-key partitionKey1 --region us-west-1

Then use the OpenSearch Service console or OpenSearch Dashboards to verify that lambda-kine-index contains a document. You can also use the following request:

GET https://domain-name/lambda-kine-index/_search
{
  "hits" : [
    {
      "_index": "lambda-kine-index",
      "_type": "_doc",
      "_id": "shardId-000000000000:49583511615762699495012960821421456686529436680496087042",
      "_score": 1,
      "_source": {
        "timestamp": 1523648740.051,
        "message": "My test data.",
        "id": "shardId-000000000000:49583511615762699495012960821421456686529436680496087042"
      }
    }
  ]
}

Loading streaming data from Amazon DynamoDB

You can use Amazon Lambda to send data to your OpenSearch Service domain from Amazon DynamoDB. New data that arrives in the database table triggers an event notification to Lambda, which then runs your custom code to perform the indexing.

Prerequisites

Before proceeding, you must have the following resources.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "es:ESHttpPost",
        "es:ESHttpPut",
        "dynamodb:DescribeStream",
        "dynamodb:GetRecords",
        "dynamodb:GetShardIterator",
        "dynamodb:ListStreams",
        "logs:CreateLogGroup",
        "logs:CreateLogStream",
        "logs:PutLogEvents"
      ],
      "Resource": "*"
    }
  ]
}

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Create the Lambda function

Follow the instructions in Create the Lambda deployment package, but create a directory named ddb-to-opensearch and use the following code for sample.py:

import boto3
import requests
from requests_aws4auth import AWS4Auth

region = '' # e.g. us-east-1
service = 'es'
credentials = boto3.Session().get_credentials()
awsauth = AWS4Auth(credentials.access_key, credentials.secret_key, region, service, session_token=credentials.token)

host = '' # the OpenSearch Service domain, e.g. https://search-mydomain.us-west-1.es.amazonaws.com
index = 'lambda-index'
datatype = '_doc'
url = host + '/' + index + '/' + datatype + '/'

headers = { "Content-Type": "application/json" }

def handler(event, context):
    count = 0
    for record in event['Records']:
        # Get the primary key for use as the OpenSearch ID
        id = record['dynamodb']['Keys']['id']['S']

        if record['eventName'] == 'REMOVE':
            r = requests.delete(url + id, auth=awsauth)
        else:
            document = record['dynamodb']['NewImage']
            r = requests.put(url + id, auth=awsauth, json=document, headers=headers)
        count += 1
    return str(count) + ' records processed.'

Edit the variables for region and host.

Install pip if you haven't already, then use the following commands to install your dependencies:

cd ddb-to-opensearch

pip install --target ./package requests
pip install --target ./package requests_aws4auth

Then follow the instructions in Create the Lambda function, but specify the IAM role from Prerequisites and the following settings for the trigger:

To learn more, see Process New Items with DynamoDB Streams and Lambda in the Amazon DynamoDB Developer Guide.

At this point, you have a complete set of resources: a DynamoDB table for your source data, a DynamoDB stream of changes to the table, a function that runs after your source data changes and indexes those changes, and an OpenSearch Service domain for searching and visualization.

Test the Lambda function

After you create the function, you can test it by adding a new item to the DynamoDB table using the Amazon CLI:

aws dynamodb put-item --table-name test --item '{"director": {"S": "Kevin Costner"},"id": {"S": "00001"},"title": {"S": "The Postman"}}' --region us-west-1

Then use the OpenSearch Service console or OpenSearch Dashboards to verify that lambda-index contains a document. You can also use the following request:

GET https://domain-name/lambda-index/_doc/00001
{
    "_index": "lambda-index",
    "_type": "_doc",
    "_id": "00001",
    "_version": 1,
    "found": true,
    "_source": {
        "director": {
            "S": "Kevin Costner"
        },
        "id": {
            "S": "00001"
        },
        "title": {
            "S": "The Postman"
        }
    }
}

Loading streaming data from Amazon Data Firehose

Firehose supports OpenSearch Service as a delivery destination. For instructions about how to load streaming data into OpenSearch Service, see Creating a Kinesis Data Firehose Delivery Stream and Choose OpenSearch Service for Your Destination in the Amazon Data Firehose Developer Guide.

Before you load data into OpenSearch Service, you might need to perform transforms on the data. To learn more about using Lambda functions to perform this task, see Amazon Kinesis Data Firehose Data Transformation in the same guide.

As you configure a delivery stream, Firehose features a "one-click" IAM role that gives it the resource access it needs to send data to OpenSearch Service, back up data on Amazon S3, and transform data using Lambda. Because of the complexity involved in creating such a role manually, we recommend using the provided role.

Loading streaming data from Amazon CloudWatch

You can load streaming data from CloudWatch Logs to your OpenSearch Service domain by using a CloudWatch Logs subscription. For information about Amazon CloudWatch subscriptions, see Real-time processing of log data with subscriptions. For configuration information, see Streaming CloudWatch Logs data to Amazon OpenSearch Service in the Amazon CloudWatch Developer Guide.

Loading streaming data from Amazon IoT

You can send data from Amazon IoT using rules. To learn more, see the OpenSearch action in the Amazon IoT Developer Guide.