Tag - MongoDB

Beyond Relational Databases: A Focus on Redis, MongoDB, and ClickHouse

Beyond Relational Databases

Many of us use and love relational databases… until we try and use them for purposes that aren’t their strong point. Queues, caches, catalogs, unstructured data, counters, and many other use cases, can be solved with relational databases, but are better served by alternative options.
Recently, Marcos Albe, Support Engineer at Percona, examined the goals, […]

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Backup and Restore of MongoDB Deployment on Kubernetes

backup MongoDB Kubernetes

Every database environment should require a robust backup strategy as its a fundamental requirement to running a successful and robust database. No matter the size of the database, the function of the application, or how technologically advanced a company is, backups are now a requirement for everyone.
As a Solutions Engineers, we speak to database […]

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On-Demand Webinar: Securing MongoDB

Securing MongoDB

MongoDB, like most any other commercial database you might care to name, has these five security subsystems:

Authentication
Authorization
Network Encryption
Data-at-rest Encryption
Auditing

In this on-demand webinar, you’ll first learn which threat each of the subsystems addresses. Second, you’ll learn which parts probably differ, by convention, to other databases you know. And lastly, you’ll receive some practical “where” and […]

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MongoDB Checkpointing Woes

MongoDB Checkpointing Woes

In my recent post Evaluating MongoDB Under Python TPCC 1000W Workload with MongoDB benchmarks, I showed an average throughput for a prolonged period of time (900sec or 1800sec), and the average throughput tended to smooth and hide problems.
But if we zoom in to 1-sec resolution for WiredTiger dashboard (Available in the Percona Monitoring and […]

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Evaluating MongoDB Under Python TPCC 1000W Workload

evaluting mongodb python tpcc

Following my blog post Evaluating the Python TPCC MongoDB Benchmark, I wanted to evaluate how MongoDB performs under workload with a bigger dataset. This time I will load a 1000 Warehouses dataset, which in raw format should equal to 100GB of data.
For the comparison, I will use the same hardware and the same MongoDB […]

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