'Elastic search with Google Big Query
I have the event logs loaded in elasticsearch engine and I visualise it using Kibana. My event logs are actually stored in the Google Big Query table. Currently I am dumping the json files to a Google bucket and download it to a local drive. Then using logstash, I move the json files from the local drive to the elastic search engine.
Now, I am trying to automate the process by establishing the connection between google big query and elastic search. From what I have read, I understand that there is a output connector which sends the data from elastic search to Google big query but not vice versa. Just wondering whether I should upload the json file to a kubernete cluster and then establish the connection between the cluster and Elastic search engine.
Any help with this regard would be appreciated.
elasticsearchSolution 1:[1]
Although this solution may be a little complex, I suggest some solution that you use Google Storage Connector with ES-Hadoop. These two are very mature and used in production-grade by many great companies.
Logstash over a lot of pods on Kubernetes will be very expensive and - I think - not a very nice, resilient and scalable approach.
Solution 2:[2]
Apache Beam has connectors for BigQuery and Elastic Search, I would definitly perform this using DataFlow so you don´t need to implement a complex ETL and staging storage. You can read the data from BigQuery using BigQueryIO.Read.from (take a look to this if performance is important BigQueryIO Read vs fromQuery) and load it into ElasticSearch using ElasticsearchIO.write()
Refer this how read data from BigQuery Dataflow
Elastic Search indexing
UPDATED 2019-06-24
Recently this year was release BigQuery Storage API which improve the parallelism to extract data from BigQuery and is natively supported by DataFlow. Refer to https://beam.apache.org/documentation/io/built-in/google-bigquery/#storage-api for more details.
From the documentation
The BigQuery Storage API allows you to directly access tables in BigQuery storage. As a result, your pipeline can read from BigQuery storage faster than previously possible.
Solution 3:[3]
I have recently worked on a similar pipeline. A workflow I would suggest would either use the mentioned Google storage connector, or other methods to read your json files into a spark job. You should be able to quickly and easily transform your data, and then use the elasticsearch-spark plugin to load that data into your Elasticsearch cluster.
You can use Google Cloud Dataproc or Cloud Dataflow to run and schedule your job.
Solution 4:[4]
As of 2021, there is a Dataflow template that allows a "GCP native" connection between BigQuery and ElasticSearch More information here in a blog post by elastic.co Further documentation and step by step process by google
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | Allan Sene |
| Solution 2 | |
| Solution 3 | Cory Grinstead |
| Solution 4 | Ismail H |