Bitmaps (also called bit arrays, bit vectors etc.) is the data structure that immediately pops in your head when the need is to map boolean information for a huge domain into a compact representation. It is a very popular data structure whenever memory space is at a premium: OS kernels (memory pages, inodes etc), digital imaging etc.
Backup and restore workflows are extremely important for any production MongoDB cluster. Apart from the actual functionality of backup and restore you also have to consider other non functionals like availabilty of backups, security, recovery time, recovery granularity etc. At a high level you have three options to backup your mongodb server
MongoDB gives you a number of tools to manage long running operations in the system. It is extremely important to keep track of operations running on your production server at any point of time – in some cases you might have rouge queries or index builds that are killing the performance of your server.
All users created in MongoDB 3.x are created with SCRAM-SHA1 which breaks backward compatibility with tools that expect MongoDB-CR. There is a laundry list of tools and drivers that have not yet been updated to support SCRAM-SHA1. E.g Robomongo, MongoVUE etc.
We are happy to announce that you can now bring your own SSL certificates to configure on your mongo clusters. This enables you to have end to end control over the SSL infrastructure of your application setup.
We are happy to announce the public availability of our slow query analyzer for MongoDB! Using the slow query analyzer you can quickly identify slow queries on any of your servers in a particular period of time. By default “slow queries” are defined as queries that take longer than 100ms.
If you are running a Mongodb replica set in a public cloud environment for any reasonable length of time the odds are that you have experienced a ‘rollback’. It does sound daunting but there are simple steps to recover your data in case your system experiences a rollback.
MongoDB supports regular expressions using the $regex operator. However these regular expression queries have a downside, all but one type of regex makes poor use of indexes and results in performance problems. For a production server with large amounts of data, a bad regex query can bring your server to its knees.
In addition to scalar indexes (ascending, descending) MongoDB also supports ‘hashed’ indexes. When you use a hashed index on a field mongodb computes a hash of the field value and stores the hash in the index. Hashed indexes support only equality comparison and do not support range queries. Hashed indexes are typically used in sharding scenarios.