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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 252
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

RedisĀ® 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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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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The evolution of single-core bandwidth in multicore processors

John McCalpin

The primary metric for memory bandwidth in multicore processors is that maximum sustained performance when using many cores. This post is about a secondary performance characteristic — sustained memory bandwidth for a single thread running on a single core. buffer copies for filesystem access) with a single thread.

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SLOG: serializable, low-latency, geo-replicated transactions

The Morning Paper

SLOG: serializable, low-latency, geo-replicated transactions Ren et al., Strict serializability reduces application code complexity and bugs, since it behaves like a system that is running on a single machine processing transactions sequentially. Thatā€™s where SLOG (Serializable LOw-latency, Geo-replicated transactions) comes in.

Latency 70
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The Surprising Effectiveness of Non-Overlapping, Sensitivity-Based Performance Models

John McCalpin

This was a keynote presentation at the “2nd International Workshop on Performance Modeling: Methods and Applications” (PMMA16), June 23, 2016, Frankfurt, Germany (in conjunction with ISC16 ). This data is from the 2007 presentation.

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The Performance Inequality Gap, 2024

Alex Russell

The usual caveats also apply: Performance is a deep and nuanced domain, and much can go wrong beyond content size and composition. How sites manage resources after-load can have a big impact on perceived performance. Until and unless teams have better data about their performance, the global baseline budget should be enforced.