To take the Elasticsearch backup in S3 buckets, you have to install the S3 repository plugin on every Elasticsearch node, using following command: sudo bin/elasticsearch-plugin install repository-s3Īnd then register a repository inside your AWS s3 bucket, curl -X PUT "localhost:9200/_snapshot/test_s3_repository?pretty" -H 'Content-Type: application/json' -d' The snapshot API does not offer queryable backup, however. You can delete old snapshots easily, and the recovery of snapshots is super easy to configure. The benefit of snapshots is that they are incremental in nature. Where, all the Mongodb configurations can be done inside only a single file which is found under /etc/nf Backup Recoveryīoth Elasticsearch and MongoDB offer backup and recovery functionality by default.Įlasticsearch performs incremental backups using _snapshot REST endpoint with the help of plugins, and its backup destinations can vary from file systems to cloud storage. You will find configuration files for Elasticsearch under /etc/elasticsearch/config directory as shown below: config |- elasticsearch.keystore |- elasticsearch.yml |- jvm.options |- log4j2.properties |- role_mapping.yml |- roles.yml |- users `- users_roles All the following configuration options are shown as per Linux Operating System. Once you install the package, the default configurations are good to start with, but here are some important configuration parameters that you should modify before taking them into production. The installation package of both Elasticsearch and MongoDB are available under all flavour of Linux, windows and Mac operating systems. MongoDB also supports full-text queries with the help of text-based indexes, but its search speed is slow and it lacks the tokenizers and analyzers that come with a search server Configurations Files On the other hand, when the data is in NoSQL format and you need a highly scalable database which requires CRUD operations without full-text search support, MongoDB is a reliable choice. Elasticsearch also wins the race when it comes to log analytics, since not only does it offer a wide range of aggregation queries, it also supports products like Kibana, Logstash, and beats-all of which make log analysis much easier. Elasticsearch will always be the better choice when full-text search is a requirement. Your use case will be critical in deciding which technology is the perfect fit. Let’s look at the differences between them in other areas. Elasticsearch is primarily a search server, while MongoDB is primarily a database. That said, they differ greatly in nature. MongoDB: A Detailed ComparisonĪs illustrated above, these technologies have a lot of similarities in their designs and features. The biggest limitations of MongoDB are its inability to provide full-text search at speed and its lack of some search functions, like tokenizing text. Some of the core features of MongoDB are: As with Elasticsearch, each record in MongoDB enters storage as a JSON object we call a “document.” MongoDB is also schemaless database that supports built-in security features like authentication, access control, and encryption. In MongoDB, you can create multiple databases, and each database can have multiple collections (tables). MongoDB is a document-oriented database written in C++ with design in mind to handle terabytes of data spread across multiple geolocations. It has a few limitations that need to be taken into account when choosing the right data store for your application. Some of the core features of Elasticsearch include:ĭespite having a rich list of feature sets, Elasticsearch is not the perfect data store for all scenarios. Each record in Elasticsearch is stored as a JSON object and is called a “document.” It organizes data under a namespace, has a defined schema, and can be divided into multiple shards for horizontal scaling. An index in Elasticsearch is similar to a database. It has been built on top of Apache Lucene and extends Lucene’s functionality with HTTP web interface and data distribution using the index and shards concept. About ElasticsearchĮlasticsearch is an open-source, Java-written, distributed RESTful search engine. MongoDB and examine differences between these two databases in a number of areas. This blog post will pit Elasticsearch vs. There are differences between the two technologies, however, and it’s important to understand these differences in order to choose the right one for your use case. Both of these technologies are highly scalable and have document-oriented design at the core. Elasticsearch and MongoDB are the two most popular distributed datastores used to manage NoSQL data.
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