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MySQL on Azure Performance Benchmark – ScaleGrid vs. Azure Database

Scalegrid

MySQL Azure Performance Benchmark. In this benchmark report, we compare MySQL hosting on Azure at ScaleGrid vs. Azure Database for MySQL across these three workload scenarios: Read-Intensive Workload: 80% reads and 20% writes. Benchmark configurations. MySQL Server Configuration. Just getting started? Standard_Ds2_v2.

Azure 299
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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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Running the ML-Perf Storage benchmark on Nutanix files.

n0derunner

Some technical notes on our submission to the benchmark committee. Background For the past few months engineers from Nutanix have been participating in the MLPerftm Storage benchmark which is designed to measure the storage performance required for ML training workloads. appeared first on n0derunner.

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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. On MySQL, we saw a 1.5X

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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. Understanding Redis Performance Indicators Redis is designed to handle high traffic and low latency with its in-memory data store and efficient data structures. <code> 127.0.0.1:6379>

Metrics 130
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Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook

The Morning Paper

Characterizing, modeling, and benchmarking RocksDB key-value workloads at Facebook , Cao et al., Or in the case of key-value stores, what you benchmark. So if you want to design a system that will offer good real-world performance, it’s really useful to have benchmarks that accurately represent real-world workloads.

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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. ibd > t1.sdi

Cache 85