Solr and ElasticSearch are both open-source search engines having a common base; Apache Lucene. Each of these offers:
- Dispersed full-text search
- Almost real-time indexing
- NoSQL aspects
Additionally, ElasticSearch offers geo-search, multi-tenancy, powerful query-DSL, sharding and replication, which is why it is more in demand.
In brief, this procedure can be divided into three steps:
- Supplying ES instances
- Build a synchronizing pipeline for MySQL-ES
- Create a query processor for ES
Provisioning ES instances needs to be carried out taking into account security measures, backup timelines and volume of work. Putting it differently, you sustain alternate steps suppose an instance returns with poor health, duplicate and backup data in the case of an unintentional data loss, call into existence a range of instances for load-balancing and more. The potential number of concurrent requests is the determining factor that is the basis of deciding the size (in terms of CPU/memory) of every instance.
If there is an already present syncing pipeline for MySQL-Solr but probably due to the variations within the indexing structure, reusing it would not be an alternative at hand. An entirely new pipeline for ES needs to be written. Also, a new query processor would need to be written, considering the fact that the query response should match the perfect precision with Solr’s query response. If this does not happen and there is a mismatch, the services that rely upon the search APIs would fail to work. Quite an amount of labor, research and experimentation recapitulations go into these two steps due to this condition.
As a result, the infrastructure is prepared for the purpose of doing indexes and searches. This is the point where two additional tasks are required to be carried out for bringing the project to its conclusion: back-populate the prevailing data to ES and getting to connect the ES with the comprehensive system.
When this step is reached, depending on whether downtime can be acceptable, all that needs to be done is to remove Solr and replace it with ES. This makes it all ready to roll. However, there can be a scenario where it becomes inevitable to apply a double-write pipeline, just to make Solr as well as ES synchronize with MySQL simultaneously. In this case, every query would still be taken care of by Solr. Prior to disassociating Solr, the data needs to be back-populated. Back-populating data is not at all a tough task. All that needs to be done is to run indexing for the entire database. If the amount of data is huge, this can be a time-consuming task.
The migration is done. However, the final task that needs to be done is to locate the appropriate server configuration for ES clusters. For this, firstly a collection of likely configurations need to be prepared and implemented for a timeframe and collated data over runtime and memory usage. The toughest part of the entire process is to keep the search API completely operational for the entire duration of the migration.
Do you have the internal experts to cover all the areas where deep experience is required to handle the Solr migration? It’s very important to have the technical expertise for solr to elasticsearch migration. A Solr support service provider can help you with professional maintenance and your Apache Solr implementation or migration project.