Aleksey Charapko
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One Page Summary: Incremental, Iterative Processing with Timely Dataflow
This paper describes Naiad distributed computation system. Naiad uses dataflow model to represent the computations, but it aims to be a general dataflow framework in contrast to other specialized approaches such as TensorFlow. Similarly to other dataflow systems, the computations are represented as graphs, where vertices represent data and operations and edges carry the data…
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Is Java Fast Enough for Distributed Applications?
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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Gorilla – Facebook’s Cache for Time Series Data
Facebook operates a huge infrastructure that needs to be constantly monitored for performance and stability. Such monitoring collects huge amounts of data that must be easily accessible to various diagnosis and anomaly detection tools in order to quickly identify and react to possible issues. Many of such parameters can be represented as real-valued time series.…
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Pivot Tracing Part 2
After looking more at Pivot Tracing tool described in my earlier post, I asked myself about the limitations of such monitoring approach. Pivot tracing is not a universal tool, so it appears that there are few problems it does not address well enough. The basic idea of the Pivot Tracing is to collect the information…
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Review – Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems
Debugging can be a nightmare for software engineers, it is even more so in the distributed systems that span many machines in potentially more than one datacenter. Unfortunately, many of the debugging and monitoring techniques for such large system do not differ much from the methods used to debug and monitor simple single-machine software. Logs…
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Review: Implementing Linearizability at Large Scale and Low Latency
In this post I will talk about Implementing Linearizability at Large Scale and Low Latency SOSP 2015 paper. Linearizability, the strongest form of consistency, can be very important in large scale data storage systems, although many such systems either do not implement linearizability or do not fully expose serializable operation to the clients. The later type…
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Recent Posts
- HoliPaxos: Towards More Predictable Performance in State Machine Replication
- Fall 2025 Reading List (##201-210)
- Paper #196. The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling
- Paper #193. Databases in the Era of Memory-Centric Computing
- Paper #192. OLTP Through the Looking Glass 16 Years Later: Communication is the New Bottleneck
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