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Showing content from https://github.com/jalalmostafa/SciTS below:

jalalmostafa/SciTS: A tool to benchmark Time-series databases

A tool to benchmark Time-series on different databases

reworked architecture
adds mixed, online workloads
adds regular and irregular ingestion modes.
adds multiple values per time series ("Dimensions")
adds limited queries
adds CLI arguments
adds ClientLatency metric to measure differences in local processing.

for questions on these features, please contact info@saninfo.de

Requires .NET 7.x cross-platform framework.

Please cite our work:

Jalal Mostafa, Sara Wehbi, Suren Chilingaryan, and Andreas Kopmann. 2022. SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things. In 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). Association for Computing Machinery, New York, NY, USA, Article 12, 1–11. https://doi.org/10.1145/3538712.3538723

@inproceedings{10.1145/3538712.3538723,
    author = {Mostafa, Jalal and Wehbi, Sara and Chilingaryan, Suren and Kopmann, Andreas},
    title = {SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things},
    year = {2022},☺
    isbn = {9781450396677},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3538712.3538723},
    doi = {10.1145/3538712.3538723},
    abstract = {Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.},
    booktitle = {Proceedings of the 34th International Conference on Scientific and Statistical Database Management},
    articleno = {12},
    numpages = {11},
    keywords = {time-series databases, database management systems, industrial internet of things, scientific experiments, sensor data, time-series},
    location = {Copenhagen, Denmark},
    series = {SSDBM '22}
}

Create your workload as App.config (case-sensitive) in BenchmarkTool.
Edit the connection strings to your database servers in the workload file.
Choose the target database in the workload file using TargetDatabase element.
run dotnet run --project BenchmarkTool write if it's an ingestion workload, and dotnet run --project BenchmarkTool read if it's a query workload. x. Use Scripts/ccache.sh <database-service-name> to clear the cache between query tests.

Additional Command Line options:

dotnet run --project BenchmarkTool [action] [regular/irregular] [DatabaseNameDB]

Available Actions:

read: start the specified retrieval and aggregation workloads.
write: start the ingestion across specified batchsize, number of clients, dimensions.
mixed-AggQueries: start the online, mixed workload benchmark as a mixture of aggregated quieries and Ingestion-Parameters
mixed-LimitedQueries: start the online, mixed workload benchmark as a mixture of queried and ingested datapoints according the specified percentage parameter and the requested Ingestion-Parameters. E.g. 100% means that as much datapoints are retrieved as ingested.

System Metrics using Glances

This tool uses glances.

Install glances with all plugins on the database server using pip install glances[all]
Run glances REST API on the database server using glances -w --disable-webui

Workload Definition Files

you can open Default-App.config edit it and save it as App.config. It has following content:

<?xml version="1.0" encoding="utf-8"?>

<configuration>
    <appSettings>
<!-- Attention: This file "AppDefault.config" is to be renamed in "App.config", after updating the "###" and other fields.  -->
  
    <!-- Datalayerts connection settings -->
        <add key="DatalayertsConnection" value="https://datalayerts.com" />
        <add key="DatalayertsUser" value="###" />
        <add key="DatalayertsPassword" value="###" />

    <!-- Postgres connection settings -->
        <add key="PostgresConnection" value="Server=localhost;Port=5432;Database=postgres;User Id=postgres;Password=###;" />

    <!-- Timescale connection settings -->
        <add key="TimescaleConnection" value="Server=localhost;Port=6432;Database=postgres;User Id=postgres;Password=###;CommandTimeout=300" />

    <!-- InfluxDB connection settings --> 
        <add key="InfluxDBHost" value="http://localhost:8086" />  
        <add key="InfluxDBToken" value="u7Ek4P5s0Nle61QQF1nNA3ywL1JYZky6rHRXxkPBX5bY4H3YFJ6T4KApWSRhaKNj_kHgx70ZLBowB6Di4t2YXg==" />
        <add key="InfluxDBBucket" value="scitsdb" />
        <add key="InfluxDBOrganization" value="scits" />  

    <!-- Clickhouse connection settings -->
        <add key="ClickhouseHost" value="localhost" />
        <add key="ClickhousePort" value="9000" />
        <add key="ClickhouseUser" value="default" />
        <add key="ClickhouseDatabase" value="default" />
 
    <!-- General Settings-->
          <add key="PrintModeEnabled" value="false" />
          <add key="TestRetries" value="2" />
          <add key="DaySpan" value="1" />
        <!-- Could be: DummyDB,  PostgresDB , DatalayertsDB , ClickhouseDB , TimescaleDB , InfluxDB -->
          <add key="TargetDatabase" value="DummyDB" />
          <add key="StartTime" value="2022-01-01T00:00:00.00" />
          <add key="RegularTsScaleMilliseconds" value="1000" /> 
        <!-- Where to store metrics files: The Programm will split the files in "[...]Read.csv" and "[...]Write.csv" -->
          <add key="MetricsCSVPath" value="Metrics_Source_Month-Day" />
          
    <!-- System Metrics Options -->
          <add key="GlancesOutput" value="Glances_Source_Month-Day.csv"/>
          <add key="GlancesUrl" value="http://localhost:61208" />
          <add key="GlancesDatabasePid" value="1" />
          <add key="GlancesPeriod" value="1" />
          <add key="GlancesNIC" value="lo" />
          <add key="GlancesDisk" value="sda1" />
          <add key="GlancesStorageFileSystem" value="/" />
        <!-- Insert multiple dimensionnrs, e.g.  1,6 ,12 ,50, 100, -->
          <add key="DataDimensionsNrOptions" value="1,6" />  

    <!-- Read Query Options -->
        <!-- Could be: Agg, All, RangeQueryRawData, RangeQueryRawAllDimsData, RangeQueryRawLimitedData, RangeQueryRawAllDimsLimitedData  RangeQueryAggData, OutOfRangeQuery, DifferenceAggQuery, STDDevQuery -->
          <add key="QueryType" value="All" />
          <add key="AggregationIntervalHour" value="1" />
          <add key="DurationMinutes" value="60" />
          <add key="SensorsFilter" value="1,2,3,4" /> <!--  or "All" -->
          <add key="SensorID" value="1" />
          <add key="MaxValue" value="0.9" />
          <add key="MinValue" value="0.1" />
          <add key="FirstSensorID" value="1" />
          <add key="SecondSensorID" value="2" />

    <!-- Ingestion -->
        <!-- Could be: regular, irregular -->
          <add key="IngestionType" value="regular" /> 
        <!-- Coulde be:  33, 100 , 300  -->
          <add key="MixedWLPercentageOptions" value="33, 100,300" />
        <!-- Could be: array, column. Array is not fully implemented in all DBMS. -->
          <add key="MultiDimensionStorageType" value="column" />
        <!-- 10, 1000, 5000, 10000 , 50000 -->
          <add key="BatchSizeOptions" value=" 100 , 1000, 6000 " />
        <!-- Number of concurrent clients e.g.(1,8,16) must be less than sensors. BatchSizes will be shared out between the clients -->
          <add key="ClientNumberOptions" value="1 , 8" />
          <add key="SensorNumber" value="100" />   

    </appSettings>

</configuration>

You can choose from the available workloads by choosing a *.config file from Workloads folder. The file to workload mapping is as follow:

Workload Workload file 2022 WLs ----------- Q1 query-q1.config Q2 query-q2.config Q3 query-q3.config Q4 query-q4.config Q5 query-q5.config Batching ingestion-batching-1client.config Concurrency ingestion-batching-nclients.config Scaling ingestion-scaling.config ............. .................................... Collection of 2023 WLs test2023.sh

We discovered abnormal high latencies and other failures with NPGSQL, so we embedded a python script which does the queries. Therefore you need to configure the python location. In case of python 3.10, e.g. use "whereis libpython3.10.so", and copy this path. Then you go into TimescaleDB.cd, and edit the string in line 134 [ Runtime.PythonDLL = "/usr/lib/Architecture-linux-gnu/libpython3.10.so"; ]

So it points to your python location

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