5-minute tutorial

Copy Data from Google Cloud Spanner to Elasticsearch with ingestr

Learn how to move Google Cloud Spanner data into Elasticsearch with a repeatable CLI workflow using ingestr.

One command Zero code Production ready

What you'll learn

How to install and set up ingestr in seconds
Connect to Google Cloud Spanner and Elasticsearch with proper authentication
Copy entire tables or specific data with a single command
Set up incremental loading for continuous data synchronization

Prerequisites

  • Python 3.8 or higher installed
  • Google Cloud service account
  • Spanner database access
  • Elasticsearch cluster access

Step 1: Install ingestr

Install ingestr in seconds using pip. Choose the method that works best for you:

Recommended: Using uv (fastest)

# Install uv first if you haven't already
pip install uv

# Run ingestr using uvx
uvx ingestr

Alternative: Global installation

# Install globally using uv
uv pip install --system ingestr

# Or using standard pip
pip install ingestr

Verify installation: Run ingestr --version to confirm it's installed correctly.

Step 2: Your First Migration

Let's copy a table from Google Cloud Spanner to Elasticsearch. This example shows a complete, working command you can adapt to your needs.

Set up your connections

Google Cloud Spanner connection format:

spanner://projects/<project>/instances/<instance>/databases/<database>?credentials_path=<credentials_path>

Parameters:

  • • project: Google Cloud project ID
  • • instance: Spanner instance ID
  • • database: Spanner database name
  • • credentials_path: Path to Google Cloud service account JSON file

Elasticsearch connection format:

elasticsearch://<username>:<password>@<host>:<port>?ssl=<ssl>&verify_certs=<verify_certs>

Parameters:

  • • username: Elasticsearch username (if security enabled)
  • • password: Elasticsearch password (if security enabled)
  • • host: Elasticsearch host
  • • port: Elasticsearch port (default: 9200)
  • • ssl: Enable SSL/TLS
  • • verify_certs: Verify SSL certificates

Run your first copy

Copy the entire users table from Google Cloud Spanner to Elasticsearch:

ingestr ingest \
    --source-uri 'spanner://projects/my-project/instances/my-instance/databases/my-db?credentials_path=/path/to/creds.json' \
    --source-table 'public.users' \
    --dest-uri 'elasticsearch://elastic:password@localhost:9200?ssl=false&verify_certs=false' \
    --dest-table 'raw.users'

What this does:

  • • Connects to your Google Cloud Spanner database
  • • Reads all data from the specified table
  • • Creates the table in Elasticsearch if needed
  • • Copies all rows to the destination

Command breakdown:

  • --source-uri Your source database
  • --source-table Table to copy from
  • --dest-uri Your destination
  • --dest-table Where to write data

Step 3: Verify your data

After the migration completes, verify your data was copied correctly:

Check row count in Elasticsearch:

-- Run this in Elasticsearch
SELECT COUNT(*) as row_count 
FROM raw.users;

-- Check a sample of the data
SELECT * 
FROM raw.users 
LIMIT 10;

Advanced Patterns

Once you've mastered the basics, use these patterns for production workloads.

Only copy new or updated records since the last sync. Perfect for daily updates. 

ingestr ingest \
    --source-uri 'spanner://projects/my-project/instances/my-instance/databases/my-db?credentials_path=/path/to/creds.json' \
    --source-table 'public.orders' \
    --dest-uri 'elasticsearch://elastic:password@localhost:9200?ssl=false&verify_certs=false' \
    --dest-table 'raw.orders' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key order_id

How it works: The merge strategy updates existing rows and inserts new ones based on the primary key. Only rows where updated_at has changed will be processed.

Common Use Cases

Ready-to-use commands for typical Google Cloud Spanner to Elasticsearch scenarios.

Daily Customer Data Sync

Keep your analytics warehouse updated with the latest customer information every night. 

# Add this to your cron job or scheduler
ingestr ingest \
    --source-uri 'spanner://projects/my-project/instances/my-instance/databases/my-db?credentials_path=/path/to/creds.json' \
    --source-table 'public.customers' \
    --dest-uri 'elasticsearch://elastic:password@localhost:9200?ssl=false&verify_certs=false' \
    --dest-table 'analytics.customers' \
    --incremental-strategy merge \
    --incremental-key updated_at \
    --primary-key customer_id

Historical Data Migration

One-time migration of all historical records to your data warehouse. 

# One-time full table copy
ingestr ingest \
    --source-uri 'spanner://projects/my-project/instances/my-instance/databases/my-db?credentials_path=/path/to/creds.json' \
    --source-table 'public.transactions' \
    --dest-uri 'elasticsearch://elastic:password@localhost:9200?ssl=false&verify_certs=false' \
    --dest-table 'warehouse.transactions_historical'

Development Environment Sync

Copy production data to your development Elasticsearch instance (with sensitive data excluded). 

# Copy sample data to development
ingestr ingest \
    --source-uri 'spanner://projects/my-project/instances/my-instance/databases/my-db?credentials_path=/path/to/creds.json' \
    --source-table 'public.products' \
    --dest-uri 'elasticsearch://elastic:password@localhost:9200?ssl=false&verify_certs=false' \
    --dest-table 'dev.products' \
    --limit 1000  # Only copy 1000 rows for testing

Choosing a Google Cloud Spanner to Elasticsearch data integration tool

If you're comparing ways to move Google Cloud Spanner data into Elasticsearch, start with the path you can run locally, review in code, and schedule later.

What is the best data integration tool to move data from Google Cloud Spanner to Elasticsearch?

ingestr is a good fit when you want an open-source CLI for Google Cloud Spanner to Elasticsearch ingestion. You can run it from your terminal, CI, or a scheduled job, then move the same pipeline into Bruin Cloud when you need orchestration, lineage, and monitoring.

Can this run as an incremental pipeline?

Yes. Use snapshot-plus-incremental or time-based extraction when the source supports it. That keeps the first load simple while making later runs smaller and easier to monitor.

When should I use Bruin Cloud with ingestr?

Use Bruin Cloud when the Google Cloud Spanner to Elasticsearch pipeline needs schedules, alerts, data quality checks, audit trails, or catalog and lineage visibility for the rest of the team.

Troubleshooting Guide

Solutions to common issues when migrating from Google Cloud Spanner to Elasticsearch.

Connection refused or timeout errors

Check your connection details:

Authentication failures

Common authentication issues:

Schema or data type mismatches

Handling data type differences:

  • ingestr automatically handles most type conversions
Performance issues with large tables

Optimize large data transfers:

  • Use incremental loading to process data in chunks
  • Run migrations during off-peak hours
  • Split very large tables by date ranges using interval parameters

Ready to scale your data pipeline?

You've learned how to migrate data from Google Cloud Spanner to Elasticsearch with ingestr. For production workloads with monitoring, scheduling, and data quality checks, explore Bruin Cloud.

Star ingestr on GitHub