![]() ![]() No, there is no error in the below chart. Sharding or distributed query execution have not been evaluated. MySQL does not: no news to report on MySQL Fabric which offers sharding for MySQL. I’ve experimented with different random data sets from 10.000 to 1.000.000 documents – it really does not matter given how coarse the relative figures are that I show below.Įlasticsearch and MonoDB offer sharding out-of-the-box. Point is: all systems have a fair chance to load the entire data set into the main memory. The MySQL figure, as you will soon notice, does not count in the size of the document ID. The three systems report slightly different sizes, likely due to different storage and charset approaches. MySQL reports a data set size of ~230MB for SELECT SUM(LENGTH(product)) FROM products (UTF8MB4). Hinting or not hinting Elasticsearch does not matter much for this test, but it’s fair as we will set extra indicies for MySQL. Neither was testing full-text search in my interest nor would MySQL or MongoDB build an FT index by default. By default it would add all text fields to its FT index. Elasicsearch is a full-text (FT) search engine. The PHP script copying data from MySQL to MongoDB uses MongoDB\BSON\UTCDateTime for the date. MongoDB and Elasticsearch have been hinted about the datetype of the “in_stock_since” field. Eventually, the decision turned out as an advantage as it permits storing the data set in a SQL table.ĭetails: data size ~230MB, JSON type hints, no sharding I didn’t bother nesting data: it all started as a feature comparison. "in_stock_since": " 14:57:32" <- random dates within 7 days "material": "silk",<- "silk", "wool", "cotton", "blends", "knits", "designer fabrics" ![]() "gender": "female",<- "male", "femal", optional/not given "usage": "dress", <- "inside", "outdoor", "baby", "dress", optional/not given The documents represent fictional “products” of an online fabrics and clothing retailer, some truly random stuff: All systems are loaded with the exact same data set. The data set consists of 1.000.000 random data documents. No parameters set but the data path for on-disk storage. The stores run in a virtual machine on my notebook. MySQL 8.0 is an unreleased internal development version which shows no significant performance difference over 5.7 for this test. My testbench consists of out-of-the-box installations of Elasticsearch, MongoDB and MySQL 8.0. The setup: Elasticsearch 2.3.5, MongoDB 3.2, MySQL 8.0 All those warnings given why not share some observations and basic tipps… However, as the quoted experts noted performance matters when choosing a system: you can often work around data model properties, you cannot overcome performance deficiencies. Given how different the focus of the three systems is any performance comparison is questionable. Some believe the RBMS and Document model can be blended in one store whereas others advocate using specialized systems. The only thing they have in common is being able to store and query JSON documents to serve (orthogonal) web developer needs. And, MySQL tries to throw it’s owners flagship database from the RBDMS throne to gain the top position. Elasticsearch is the most popular enterprise search engine closely followed by Solr. Use the index, MySQL Document Store users!Ĭomparing Elasticsearch, MySQL and MongoDB comes with a touch. Sometimes the difference to Elasticsearch is huge, sometimes – just – acceptable. Sometimes the MySQL Document Store runs head-on-head with MongoDB, sometimes not. Use the index, Luke!? No: use the index, JSON! When hunting features the X DevAPI should consider I got shocked by some JSON query execution times. ![]()
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