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MySQL on Azure Performance Benchmark – ScaleGrid vs. Azure Database

Scalegrid

While Microsoft offers their own Azure Database product, there are other alternatives available that may be able to help you improve your MySQL performance. In this blog post, we compare Azure Database for MySQL vs. ScaleGrid MySQL on Azure so you can see which provider offers the best throughput and latency performance.

Azure 299
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Benchmark (YCSB) numbers for Redis, MongoDB, Couchbase2, Yugabyte and BangDB

High Scalability

An application example is a session store recording recent actions. We note that for MongoDB update latency is really very low (low is better) compared to other dbs, however the read latency is on the higher side. Application example: photo tagging; add a tag is an update, but most operations are to read tags. Conclusion.

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Implementing service-level objectives to improve software quality

Dynatrace

Instead, they can ensure that services comport with the pre-established benchmarks. When organizations implement SLOs, they can improve software development processes and application performance. This process includes benchmarking realistic SLO targets based on statistical and probabilistic analysis from Dynatrace. Reliability.

Software 259
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Redis® is an in-memory database that provides blazingly fast performance. This makes it a compelling alternative to disk-based databases when performance is a concern. You might already use ScaleGrid hosting for Redis hosting to power your performance-sensitive applications.

Metrics 130
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Maximizing Performance of AWS RDS for MySQL with Dedicated Log Volumes

Percona

A quick configuration change may do the trick in improving the performance of your AWS RDS for MySQL instance. Here, we will discuss a notable new feature in Amazon RDS, the Dedicated Log Volume (DLV), that has been introduced to boost database performance. Who can benefit from DLV? 2xlarge c5.2xlarge MySQL 8.0.31

AWS 98
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Why you should benchmark your database using stored procedures

HammerDB

HammerDB uses stored procedures to achieve maximum throughput when benchmarking your database. HammerDB has always used stored procedures as a design decision because the original benchmark was implemented as close as possible to the example workload in the TPC-C specification that uses stored procedures.

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Grafana Dashboards: A PoC Implementing the PostgreSQL Extension pg_stat_monitor

Percona

pg_stat_monitor About pg_stat_monitor is a Query Performance Monitoring tool for PostgreSQL. It collects various statistics data such as query statistics, query plan, SQL comments, and other performance insights. This allows a user to drill down into the performance of queries. And yes, it even works in PostgreSQL.