Steps in Migrating from Elasticsearch to Vespa

Starting with a proof of concept, a multi-phased migration approach is the most effective strategy for transitioning between search engines, ensuring minimal risk, stable performance, and a controlled rollout. Unlike a Big Bang migration, which can lead to workload overload, unforeseen failures, and degraded search performance, a step-by-step transition enables thorough testing, iterative refinements, and built-in fallback options. This approach reduces disruptions, allows for real-time performance validation, and ensures the new system is fully optimized before taking over production traffic.

Step 1: Establish a Dedicated Migration Team

A successful migration begins with forming a dedicated team of search engineers and system architects with expertise in existing and target search technologies. This team should define key focus areas for the migration, including:

• Architecture: Designing a scalable and performant system with the new search engine.
• Infrastructure: Planning server deployment, resource allocation, and load balancing.
• Indexing: Optimizing real-time data ingestion, ensuring minimal latency.
• Querying: Fine-tuning search logic, ranking models, and hybrid search capabilities.
• Metrics & Performance Testing: Monitoring system behavior and validating performance under production-like conditions.

Step 2: Develop and Iterate a Proof of Concept

Migration begins with developing a proof of concept (PoC) and refining it through iterations until it meets functional, performance, and relevance requirements. This typically involves defining a Vespa application package with a schema that fits the data model and ingesting a portion of the dataset. Tools like Logstash can assist with this process. During this phase, organizations may need to adjust the schema and map Elasticsearch concepts to Vespa, using resources like the Vespa glossary to guide the transition.

Once data is available in Vespa, queries need to be translated as well. Vespa offers multiple ways to express queries, similar to Elasticsearch. Those using SQL or ES|QL in Elasticsearch may find Vespa’s YQL a natural alternative. Vespa’s select syntax provides equivalent functionality for applications built on the JSON Query DSL. Aggregation queries can be adapted using Vespa’s grouping feature, which supports nested structures like Elasticsearch. 

Once functional equivalence is achieved, the next step is scaling the Vespa cluster to accommodate the full dataset and conducting performance testing. Unlike Elasticsearch, which relies on a fixed number of physical shards, Vespa distributes data across many virtual buckets. This approach simplifies scaling, eliminating the need to manually manage sharding when adding or removing nodes.

Performance optimization typically involves gradually increasing query load, monitoring key metrics, and adjusting cluster size. Vespa’s performance guides provide additional insights on capacity planning, tuning, and best practices for optimizing efficiency at scale.

Step 3: Benchmark and Stress Test Performance

To ensure the new system can handle real-world traffic loads, performance benchmarking and stress testing should be conducted. This includes:

• Measuring query throughput, indexing speed, and latency under high load.
• Scaling up traffic in controlled increments to identify potential bottlenecks.
• Optimizing system configurations to maximize efficiency and stability.

Step 4: Implement a Shadow Traffic Strategy

A shadow traffic approach should be employed before making any changes visible to users. This involves sending live queries simultaneously to existing and new search engines and comparing results in real-time. Shadow traffic allows engineers to:

• Identify discrepancies in search behavior between the two systems.
• Fine-tune ranking and indexing logic to ensure consistency.
• Validate infrastructure performance before exposing users to the new system.

Step 5: Conduct A/B Testing for Search Relevance

Once shadow testing confirms the system’s technical stability, A/B testing should be used to evaluate search relevance. Users are gradually exposed to the new search engine in controlled experiments, allowing search teams to:

• Measure user engagement, satisfaction, and relevance metrics.
• Fine-tune ranking algorithms and query execution.
• Iterate through multiple test cycles to match or exceed the previous system’s quality.

Step 6: Gradual Cutover and Full Migration

After refining search quality and validating performance, the final migration should be a gradual switch rather than an immediate transition. This ensures:

• A fallback option remains available if any unforeseen issues arise.
• Load balancing is adjusted to prevent system strain.
• Search performance remains stable as traffic progressively shifts to the new search engine.

Summary

Migrating between technologies is never straightforward, but by following this structured, multi-phase migration process, organizations can mitigate risks, maintain search quality, and transition seamlessly to a new search platform without disrupting the user experience.

If you have questions or would like to discuss this further, please contact us.