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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

Redis Server: 5.07, x86/64. MongoDB server: 4.4.2, BangDB server: 2.0.0, However, user can run the bench for as many numbers as they practically find suitable. About YCSB. Following configurations were used for the evaluation purpose. YugabyteDB:2.5.0, Couchbase2: 7.0 Beta, x86_64. Number of records: 10M. RAM: 32GB, Cores: 16.

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Why Tcl is 700% faster than Python for database benchmarking

HammerDB

Python is a popular programming language, especially for beginners, and consequently we see it occurring in places where it just shouldn’t be used, such as database benchmarking. We use stored procedures because, as the introductory post shows, using single SQL statements turns our database benchmark into a network test).

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Redis vs Memcached in 2024

Scalegrid

In this comparison of Redis vs Memcached, we strip away the complexity, focusing on each in-memory data store’s performance, scalability, and unique features. can enhance Redis by handling management tasks, backups, and scalability, facilitating global reach and easy cloud integration for global businesses.

Cache 130
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PostgreSQL: Pgpool-II Use Cases and Benefits

Percona

One of the key benefits of using PostgreSQL is its reliability, scalability, and performance. Load balancing One of the primary benefits of using pgpool-II is its ability to distribute incoming client connections across multiple PostgreSQL servers, allowing you to balance the load and increase the capacity of your database cluster.

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The top 5 reasons to run your own database benchmarks

HammerDB

Some opinions claim that “Benchmarks are meaningless”, “benchmarks are irrelevant” or “benchmarks are nothing like your real applications” However for others “Benchmarks matter,” as they “account for the processing architecture and speed, memory, storage subsystems and the database engine.”

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What Is a Workload in Cloud Computing

Scalegrid

These include popular technologies such as web servers and web applications, along with advanced solutions like distributed data stores and containerized microservices. Strategic allocation of these resources plays a crucial role in achieving scalability, cost savings, improved performance, and staying ahead of advancements in the field.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

But as companies grow and see more demand for their databases, we need to ensure that PMM also remains scalable so you don’t need to worry about its performance while tending to the rest of your environment. These updates are designed to keep databases running at peak performance and simplify database operations.