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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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View from Nutanix storage during Postgres DB benchmark

n0derunner

The post View from Nutanix storage during Postgres DB benchmark appeared first on n0derunner. Even though the log writes are sequential, they are low-concurrency and small size (looks like mostly 16K-32K). This write pattern is also a good candidate for oplog. These low-concurrency log writes also hit oplog.

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Best MySQL DigitalOcean Performance – ScaleGrid vs. DigitalOcean Managed Databases

Scalegrid

ScaleGrid provides 30% more storage on average vs. DigitalOcean for MySQL at the same affordable price. MySQL DigitalOcean Performance Benchmark. In this benchmark, we compare equivalent plan sizes between ScaleGrid MySQL on DigitalOcean and DigitalOcean Managed Databases for MySQL. Read-Intensive Throughput Benchmark.

Database 217
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Maximizing Performance of AWS RDS for MySQL with Dedicated Log Volumes

Percona

A Dedicated Log Volume (DLV) is a specialized storage volume designed to house database transaction logs separately from the volume containing the database tables. DLVs are particularly advantageous for databases with large allocated storage, high I/O per second (IOPS) requirements, or latency-sensitive workloads.

AWS 100
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A Generalized workload generator for storage IO

n0derunner

With help from the Nutanix X-Ray team I have created an IO “benchmark” which simulates a “General Server Virtualization” workload. The post A Generalized workload generator for storage IO appeared first on n0derunner.

Storage 52
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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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Measuring the importance of data quality to causal AI success

Dynatrace

It starts with implementing data governance practices, which set standards and policies for data use and management in areas such as quality, security, compliance, storage, stewardship, and integration. Fragmented and siloed data storage can create inconsistencies and redundancies.