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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, Application example: user profile cache, where profiles are constructed elsewhere (e.g., 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.

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How to use Server Timing to get backend transparency from your CDN

Speed Curve

Server-timing headers are a key tool in understanding what's happening within that black box of Time to First Byte (TTFB). Looking at the industry benchmarks for US retailers , four well-known sites have backend times that are approaching – or well beyond – that threshold.

Servers 57
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

You will need to know which monitoring metrics for Redis to watch and a tool to monitor these critical server metrics to ensure its health. Evaluating factors like hit rate, which assesses cache efficiency level, or tracking key evictions from the cache are also essential elements during the Redis monitoring process.

Metrics 130
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Impact of Querying Table Information From information_schema

Percona

On MySQL and Percona Server for MySQL , there is a schema called information_schema (I_S) which provides information about database tables, views, indexes, and more. Disclaimer : This blog post is meant to show a less-known problem but is not meant to be a serious benchmark. Results for Percona Server for MySQL 5.7

Cache 101
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20X Faster Backup Preparation With Percona XtraBackup 8.0.33-28!

Percona

The data is internally inconsistent because the server concurrently modifies the data files while they are being copied. The changes done by an uncommitted transaction can be flushed or written to the redo log by the server. Initializing a DD engine and the cache adds complexity and other server dependencies.

Cache 85
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MySQL Key Performance Indicators (KPI) With PMM

Percona

As a MySQL database administrator, keeping a close eye on the performance of your MySQL server is crucial to ensure optimal database operations. However, simply deploying a monitoring tool is not enough; you need to know which Key Performance Indicators (KPIs) to monitor to gain insights into your MySQL server’s health and performance.

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Percona Monitoring and Management 2 Scaling and Capacity Planning

Percona

Planning for resources of a PMM Server host instance can be tricky because the numbers can change depending on the DB instances being monitored by PMM. VictoriaMetrics maintains an in-memory cache for mapping active time series into internal series IDs. The CPU usage averaged 24% utilization, as you can see in the above picture.