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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

Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. offers the Software Watchdog specifically designed for this purpose.

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Kubernetes for Big Data Workloads

Abhishek Tiwari

Kubernetes has emerged as go to container orchestration platform for data engineering teams. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next. better cluster resource utilization.

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Impact of Querying Table Information From information_schema

Percona

Disclaimer : This blog post is meant to show a less-known problem but is not meant to be a serious benchmark. The percentage in degradation will vary depending on many factors {hardware, workload, number of tables, configuration, etc.}. Setup The setup consists of creating 10K tables with sysbench and adding 20 FKs to 20 tables.

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How to Assess MySQL Performance

HammerDB

Among the different components of modern software solutions, the database is one of the most critical. Benchmarking the target Two of the more popular database benchmarks for MySQL are HammerDB and sysbench. We used the first processor socket for the MySQL database and the second socket for the benchmark (sysbench or HammerDB).

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Upcoming of the learned data structures

Abhishek Tiwari

Can machine learning-based data structures i.e. learned data structures replace traditional data structures? The initial results show that learned data structures can outperform existing data structures. This has far-reaching implications how future data systems and algorithms will be designed. p = F(Key) ?

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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Existing cache and main memory compression techniques compress data in small fixed-size blocks, typically cache lines. Looking across a set of eight Java benchmarks, we find that only two of them are array dominated, the rest having between 40% to 75% of the heap footprint allocated to objects, the vast majority of which are small.

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