We are just starting to play with ES. We have very little experience with it or Google Cloud Services. There’s nothing like jumping in with both feet.
We have a CSV file that has about 10M records and about 4 Gig.
We want to ingest this into ES. But my concern is that it will go very slowly.
Once it is ingested, there will not be a lot of searching done. There will be maybe 10 of us starting to play with ES and its capabilities, so I want to keep my costs down
What kind of setup would you use to do the first injection? And then the ongoing set up for the server. Any help will be greatly appreciated.
Ingesting a large CSV file into Elasticsearch on Google Cloud and setting up an efficient, cost-effective environment requires careful planning. Here’s an optimized plan for importing your CSV data into Elasticsearch, balancing cost-efficiency and providing room for experimentation:
First Injection (Large CSV Import)
Method Options:
Google Cloud Dataflow Template:
Utilize the “GCS to Elasticsearch” Dataflow template offered by Google.
This template efficiently handles reading CSV files from GCS and indexing them into Elasticsearch.
Pros: Streamlined for CSV, manages scaling and error handling automatically.
Cons: Requires familiarity with Dataflow and potential adjustments for CSV format and Elasticsearch index mappings.
Script with Elasticsearch Client Library:
Select an Elasticsearch client library in a language you’re comfortable with (e.g., Python, Java).
Develop a script to:
Read the CSV from GCS.
Parse each row.
Send indexing requests to Elasticsearch using batching and error management.
Pros: Greater control over the process, leverages existing scripting skills.
Cons: More manual effort required for data processing and error handling.
Cost Considerations for the First Injection:
Provision a Temporary Elasticsearch Cluster: Create a new cluster specifically for the import, scaled to handle the data size. After the import, you can delete this cluster or downscale it.
Utilize Dataflow’s Auto-scaling: This feature adjusts the number of workers based on data size, optimizing processing speed and cost.
Ongoing Setup (For Experimentation)
Provision a Smaller Elasticsearch Cluster: Start with a small, single-node cluster, scaling up as needed. Choose a machine type with modest CPU and memory, and use Google Cloud’s cost calculators for budget estimation.
Enable Data Compression: Activate Elasticsearch’s data compression settings to reduce storage requirements.
Regular Usage Monitoring: Track resource utilization with Google Cloud’s tools and Elasticsearch’s APIs, adjusting the cluster size based on actual usage.
Cluster Size Recommendations (For First Injection)
Estimate Indexed Data Size: Indexing can increase data size (factor of 1.5 - 2x). Consider this when choosing the cluster size.
Check Machine Type Limits: Refer to Google Cloud documentation for storage and memory limits of different machine types.
Initial Size Estimate: For an estimated indexed data size of 10 - 12 GB, start with a 2-node cluster, each node having 16-32 GB of memory.
Monitor During Import: Watch resource utilization closely and scale up if necessary.
Additional Tips
Test with a Sample: Import a smaller data sample first to gauge indexing speed and resource needs.
Explore Elasticsearch Documentation: Gain insights into index settings, mapping, and data compression for further optimization.