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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2204.09795/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2204.09795/feed.xml" rel="self" type="application/rss+xml" /><item><title>SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things — mentioned by zX41ZdbW</title><link>https://news.ycombinator.com/item?id=34620048</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.09795:34620048</guid><pubDate>Wed, 01 Feb 2023 23:59:37 GMT</pubDate><description>&lt;p&gt;Interesting to match with the observations from the practice of using ClickHouse[1][2] for time series: 1. Reordering to SOA helps a lot - this is the whole point of column-oriented databases. 2. Specialized codecs like Gorilla[3], DoubleDelta[4], and FPC[5] lose to simply using ZSTD[6] compression in most cases, both in compression ratio and in performance. 3. Specialized time-series DBMS like InfluxDB or TimescaleDB lose to general-purpose relational OLAP DBMS like ClickHouse [7][8][9]. [1] https://clickhouse.com/blog/optimize-clickhouse-codecs-compr... [2]…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.09795/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item><item><title>SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things — mentioned by zX41ZdbW</title><link>https://news.ycombinator.com/item?id=33356493</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.09795:33356493</guid><pubDate>Thu, 27 Oct 2022 12:25:46 GMT</pubDate><description>&lt;p&gt;Here is a recent comparison: https://arxiv.org/pdf/2204.09795.pdf Although I&amp;#x27;m not sure if it is using the new IOx engine or not.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.09795/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item><item><title>SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things — mentioned by zX41ZdbW</title><link>https://news.ycombinator.com/item?id=33099822</link><guid isPermaLink="false">urn:hackerpapers:mention:2204.09795:33099822</guid><pubDate>Wed, 05 Oct 2022 18:58:34 GMT</pubDate><description>&lt;p&gt;ClickHouse supports ad-hoc analytics, real-time reporting, and time series workloads at the same time. It is perfectly suited for user-facing analytics services. It supports low-latency (&amp;lt;100ms) queries for real-time analytics as well as high query throughput (500 QPS and more) - all of this with real-time data ingestion of logs, events, and time series. Take some notable examples from the list: https://clickhouse.com/docs/en/about-us/adopters/, something around web analytics, APM, ad networks, telecom data... ClickHouse is perfectly suited for these use cases. But if you try to align these…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2204.09795/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.DB</category></item></channel></rss>
