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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

This was a chance to talk about other things I've been working on, such as the present and future of hardware performance. The video is on [youtube]: The slides are on [slideshare] or as a [PDF]: I work on many areas of performance, but recently I've had a lot of demand to talk about BPF. Ford, et al., “TCP

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory. Ford, et al., “TCP

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USENIX LISA2021 Computing Performance: On the Horizon

Brendan Gregg

This was a chance to talk about other things I've been working on, such as the present and future of hardware performance. The video is on [youtube]: The slides are [here] or as a [PDF]: first prev next last / permalink/zoom I work on many areas of performance, but recently I've had a lot of demand to talk about BPF. Ford, et al., “TCP

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USENIX SREcon APAC 2022: Computing Performance: What's on the Horizon

Brendan Gregg

My personal opinion is that I don't see a widespread need for more capacity given horizontal scaling and servers that can already exceed 1 Tbyte of DRAM; bandwidth is also helpful, but I'd be concerned about the increased latency for adding a hop to more memory. Ford, et al., “TCP

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A thorough introduction to bpftrace

Brendan Gregg

For example, iostat(1), or a monitoring agent, may tell you your average disk latency, but not the distribution of this latency. For smaller environments, it can be of more use helping eliminate latency outliers. hardware Hardware counter-based instrumentation. Block I/O latency as a histogram.

Latency 68
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Kubernetes for Big Data Workloads

Abhishek Tiwari

In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Using default scheduler's node affinity feature you can ensure that certain pods only schedule on nodes with specialized hardware like GPU, memory-optimised, I/O optimised etc. Kubernetes has a massive community support and momentum behind it.

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5 data integration trends that will define the future of ETL in 2018

Abhishek Tiwari

There are several emerging data trends that will define the future of ETL in 2018. In 2018, we anticipate that ETL will either lose relevance or the ETL process will disintegrate and be consumed by new data architectures. Leveraging the recent hardware advances. Common in-memory data interfaces. More details on this approach.