Aleksey Charapko

Performance

  • Reading Group. Evolution of Development Priorities in Key-value Stores Serving Large-scale Applications: The RocksDB Experience

    Aleksey Charapko

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    On Wednesday, we had our 26th reading group meeting, discussing RocksDB with a help of a recent experience paper: “Evolution of Development Priorities in Key-value Stores Serving Large-scale Applications: The RocksDB Experience.” Single-server key-value storage systems are crucial for so many distributed systems and databases. For distributed folks like myself, these often remain black-boxes that…

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  • Modeling Paxos Performance in Wide Area – Part 3

    Aleksey Charapko

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    Earlier I looked at modeling paxos performance in local networks, however nowadays people (companies) use paxos and its flavors in the wide area as well. Take Google Spanner and CockroachDB as an example. I was naturally curious to expand my performance model into wide area networks as well. Since our lab worked on WAN coordination…

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  • Modeling Paxos Performance – Part 2

    Aleksey Charapko

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    In the previous posts I started to explore node-scalability of paxos-style protocols. In this post I will look at processing overheads that I estimate with the help of a queue or a processing pipeline. I show how these overheads cap the performance and affect the latency at different cluster loads. I look at the scalability for…

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  • Paxos Performance Modeling – Part 1.5

    Aleksey Charapko

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    This post is a quick update/conclusion to the part 1. So, does the network variations make any impact at all? In the earlier simulation I showed some small performance degradation going from 3 to 5 nodes. The reality is that for paxos, network behavior makes very little difference on scalability, and in some cases no difference at…

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  • Do not Blame (only) Network for Your Paxos Scalability Issues. (PPM Part 1)

    Aleksey Charapko

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    In the past few months our lab has been doing a lot of work with different flavors of paxos consensus algorithm. Paxos and its numerous flavors are widely used in today’s cloud infrastructure. Distributed systems rely on it for many different tasks to ensure safe operation. For instance, coordination services use some consensus protocol flavor…

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  • One Page Summary: “milliScope: a Fine-Grained Monitoring Framework for Performance Debugging of n-Tier Web Services”

    Aleksey Charapko

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    Authors of the ICDCS2017 milliScope paper attack an interesting monitoring problem for distributed systems: detecting and determining a cause of short-lived events in the system. In particular, they address the issue of identifying very short bottlenecks (VSBs) in distributed web services. VSBs manifest themselves as performance degradation of a small number of requests, however they…

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  • Is Java Fast Enough for Distributed Applications?

    Aleksey Charapko

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    Lots of modern distributed systems are built with Java programming language, and consequently use Java Virtual Machine (JVM) as their execution environment. The list of such systems is rather large: Hadoop, Spark, HBase, Cassandra, Voldemort, ZooKeeper, BookKeeper, Kafka, and the list goes on and on. But is JVM fast enough for these systems? Anyone who…

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