Search engines are an essential part of any content website today.
It is enabling users to easily find and access the information they need.
Vespa, Elasticsearch, and Solr are 3 of the most popular search engines in the market today, each with its own unique features and capabilities.
In this article, we’ll compare them “head to head” with capabilities, features, and performance so you can make the best decision when choosing one.
|Prefix matching, gram matching
|Semantic search – ANN / KNN
|KNN integration. bert as a service, sense2vec
|Dense vector type , bert as a service, stemming
|Built in HSNW algorithm
Distilbert With Extensions
|through rewrite word/sentence embbeddings
|External learning to rank
|Ranking with ML Models, LATOR – built-in integration
|grouping, aggregations strategies
|Grouping abilities blog example
|All languages, some features are limited (e.g. tokenization)
|deploy model into the pipeline
|Easy embedding of models (ONNX and others)
|Implements Beider-Morse Phonetic Matching
|sponsored search, analytics
|rich ranking abilities, Q&A
|Yes – Apache
|Yes – by Yahoo
|elastic cloud open search
|K8s, HDFS, linux
|K8S, single machine
|Docker, RPM, K8S, ECS
|Java, Python API
Choosing the right search engine depends on a variety of factors, such as the nature of the data, the complexity of the search queries, and the performance requirements. Here are some guidelines to help you decide which search engine to use:
Ultimately, the choice of search engine depends on the specific needs and requirements of your application. You should consider factors such as the size and complexity of your data, the types of search queries you need to support, and the performance requirements of your application before making a decision.