Later we create a lambda function from this deployed S3 Bucket. The 'problem' is that this will require a lot of memory on Elasticsearch side as it has to get the JSON content, extract the binary from the BASE64, send it to Tika, create the final field, and index all that. What we do is using AWS Cli, get our lambda packaged and deployed into S3 from Linux ec2 instance. If you are using NodeJS, you can probably read the binary from S3 and then send it to elasticsearch. If _name_ = ‘_main_’: lambda_handler(None, None) Options are you pushing from beats to logstash and use logstash to write to S3, or use logstash to tail logs files. “”” Can Override the global variables using Lambda Environment Parameters “”” globalVars = docData = str(key) docData = str(obj) docData = str(obj) docData = str(obj)įor line in lines: docData = str(line) indexDocElement(es_Url, awsauth, docData) (‘File processing comlplete. Right, beats do not have an S3 output available. #!/usr/bin/python3 # -*- coding: utf-8 -*- import boto3 import json import datetime import gzip import urllib import urllib3 import logging from requests_aws4auth import AWS4Auth import requests from io import BytesIO
FILEBEATS S3 TO ELASTICSEARCH CODE
I have got the code from one of the github resource.
We are using Linux machine for deployment so make sure cli has also access using the IAM Roles with AWSLambdaExecute permission. Go into your IAM to provide necessary roles for your lambda to access S3 and Elastic Search. Just check Cluster health to check if the setup is looking good.
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