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| <svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a></nav> <ul class="sidebar-links"><li><section class="sidebar-group depth-0"><p class="sidebar-heading"><span>IoTDB User Guide (In progress)</span> <!----></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Get Started</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Overview</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Concept</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Server</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Client</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Operation Manual</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>System Tools</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Ecosystem Integration</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading"><span>Architecture</span> <span class="arrow right"></span></p> <!----></section></li><li><section class="sidebar-group collapsable depth-0"><p class="sidebar-heading open"><span>Comparison with TSDBs</span> <span class="arrow down"></span></p> <ul class="sidebar-links sidebar-group-items"><li><a href="/UserGuide/Master/Comparison/TSDB-Comparison.html" aria-current="page" class="active sidebar-link">Comparison</a></li></ul></section></li></ul> </aside> <main class="page"> <div class="content content__default"><h1 id="comparison"><a href="#comparison" class="header-anchor">#</a> Comparison</h1> <h2 id="known-time-series-database"><a href="#known-time-series-database" class="header-anchor">#</a> Known Time Series Database</h2> <p>As the time series data is more and more important, |
| several open sourced time series databases are intorduced in the world. |
| However, few of them are developed for IoT or IIoT (Industrial IoT) scenario in particular.</p> <p>We choose 3 kinds of TSDBs here.</p> <ul><li><p>InfluxDB - Native Time series database</p> <p>InfluxDB is one of the most popular TSDBs.</p> <p>Interface: InfluxQL and HTTP API</p></li> <li><p>OpenTSDB and KairosDB - Time series database based on NoSQL</p> <p>These two DBs are similar, while the first is based on HBase and the second is based on Cassandra. |
| Both of them provides RESTful style API.</p> <p>Interface: Restful API</p></li> <li><p>TimesacleDB - Time series database based on Relational Database</p> <p>Interface: SQL</p></li></ul> <p>Prometheus and Druid are also famous for time series data management. |
| However, Prometheus focuses on how to collect data, how to visualize data and how to alert warnings. |
| Druid focuses on how to analyze data with OLAP workload. We omit them here.</p> <h2 id="comparison-2"><a href="#comparison-2" class="header-anchor">#</a> Comparison</h2> <p>We compare the above time series database from two aspects: the feature comparison and the performance |
| comparison.</p> <h3 id="feature-comparison"><a href="#feature-comparison" class="header-anchor">#</a> Feature Comparison</h3> <p>I list the basic features comparison of these databases.</p> <p>Legend:</p> <ul><li>O: big support greatly</li> <li>o: support</li> <li>x: not support</li> <li>😦 : support but not very good</li> <li>?: unknown</li></ul> <h4 id="basic-features"><a href="#basic-features" class="header-anchor">#</a> Basic Features</h4> <table><thead><tr><th>TSDB</th> <th>IoTDB</th> <th>InfluxDB</th> <th>OpenTSDB</th> <th>KairosDB</th> <th>TimescaleDB</th></tr></thead> <tbody><tr><td>OpenSource</td> <td><strong>o</strong></td> <td>o</td> <td>o</td> <td><strong>o</strong></td> <td>o</td></tr> <tr><td>SQL-like</td> <td>o</td> <td>o</td> <td>x</td> <td>x</td> <td><strong>O</strong></td></tr> <tr><td>Schema</td> <td>"Tree-based, tag-based"</td> <td>tag-based</td> <td>tag-based</td> <td>tag-based</td> <td>Relational</td></tr> <tr><td>Writing out-of-order data</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>Schema-less</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>Batch insertion</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>Time range filter</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>Order by time</td> <td><strong>O</strong></td> <td>o</td> <td>x</td> <td>x</td> <td>o</td></tr> <tr><td>Value filter</td> <td>o</td> <td>o</td> <td>x</td> <td>x</td> <td>o</td></tr> <tr><td>Downsampling</td> <td><strong>O</strong></td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>Fill</td> <td><strong>O</strong></td> <td>o</td> <td>o</td> <td>x</td> <td>o</td></tr> <tr><td>LIMIT</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td> <td>o</td></tr> <tr><td>SLIMIT</td> <td>o</td> <td>o</td> <td>x</td> <td>x</td> <td>?</td></tr> <tr><td>Latest value</td> <td>O</td> <td>o</td> <td>o</td> <td>x</td> <td>o</td></tr></tbody></table> <p><strong>Details</strong></p> <ul><li><p>OpenSource:</p> <ul><li>IoTDB uses Apache License 2.0 and it is in Apache incubator.</li> <li>InfluxDB uses MIT license. However, <strong>the cluster version is not open sourced</strong>.</li> <li>OpenTSDB uses LGPL2.1, which <strong>is not compatible with Apache License</strong>.</li> <li>KairosDB uses Apache License 2.0.</li> <li>TimescaleDB uses Timescale License, which is not free for enterprise.</li></ul></li> <li><p>SQL like:</p> <ul><li>IoTDB and InfluxDB supports SQL like language. Besides, The integration of IoTDB and Calcite is alomost done (a PR has been submitted), which means IoTDB will support Standard SQL.</li> <li>OpenTSDB and KairosDB only support Rest API. Besides, IoTDB also supports Rest API (a PR has been submitted).</li> <li>TimescaleDB uses the SQL the same with PG.</li></ul></li> <li><p>Schema:</p> <ul><li><p>IoTDB: IoTDB proposes a <a href="http://iotdb.apache.org/UserGuide/Master/Concept/Data%20Model%20and%20Terminology.html" target="_blank" rel="noopener noreferrer">Tree based schema<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a>. |
| It is quite different with other TSDBs. However, the kind of schema has the following advantages:</p> <ul><li><p>In many industrial scenarios, the management of devices are hierarchical, rather than flat. |
| That is why we think a tree based schema is better than tag-value based schema.</p></li> <li><p>In many real world applications, tag names are constant. For example, a wind turbine manufacturer |
| always identify their wind turbines by which country it locates, the farm name it belongs to, and its ID in the farm. |
| So, a 4-depth tree ("root.the-country-name.the-farm-name.the-id") is fine. |
| You do not need to repeat to tell IoTDB the 2nd level of the tree is for country name, |
| the 3rd level is for farm id, etc..</p></li> <li><p>A path based time series ID definition also supports flexible queries, like "root.*.a.b.*", wehre * is wildcard character.</p></li></ul></li> <li><p>InfluxDB, KairosDB, OpenTSDB are tag-value based, which is more popular currently.</p></li> <li><p>TimescaleDB uses relational table.</p></li></ul></li> <li><p>Order by time:</p> <p>Order by time seems quite trivil for time series database. But... if we consider another feature, called align by time, |
| something becomes interesting. And, that is why we mark OpenTSDB and KairosDB unsupported.</p> <p>Actually, in each time series, all these TSDBs support order data by timestamps.</p> <p>However, OpenTSDB and KairosDB do not support order the data from different timeseries in the time order.</p> <p>Ok, considering a new case: I have two time series, one is for the wind speed in wind farm1, |
| another is for the generated energy of wind turbine1 in farm1. If we want to analyze the relation between the |
| wind speed and the generated energy, we have to know the values of both at the same time. |
| That is to say, we have to align the two time series in the time dimension.</p> <p>So, the result should be:</p> <table><thead><tr><th>timestamp</th> <th>wind speed</th> <th>generated energy</th></tr></thead> <tbody><tr><td>1</td> <td>5.0</td> <td>13.1</td></tr> <tr><td>2</td> <td>6.0</td> <td>13.3</td></tr> <tr><td>3</td> <td>null</td> <td>13.1</td></tr></tbody></table> <p>or,</p> <table><thead><tr><th>timestamp</th> <th>series name</th> <th>value</th></tr></thead> <tbody><tr><td>1</td> <td>wind speed</td> <td>5.0</td></tr> <tr><td>1</td> <td>generated energy</td> <td>13.1</td></tr> <tr><td>2</td> <td>wind speed</td> <td>6.0</td></tr> <tr><td>2</td> <td>generated energy</td> <td>13.3</td></tr> <tr><td>3</td> <td>generated energy</td> <td>13.1</td></tr></tbody></table></li></ul> <p>Though the second table format does not align data by the time dimension, but it is easy to be implemented in the client-side, |
| by just scanning data row by row.</p> <p>IoTDB supports the first table format (called align by time), InfluxDB supports the second table format.</p> <ul><li><p>Downsampling:</p> <p>Downsampling is for changing the granularity of timeseries, e.g., from 10Hz to 1Hz, or 1 point per day.</p> <p>Different with other systems, IoTDB downsamples data in real time, while others serialized downsampled data on disk.<br> |
| That is to say,</p> <ul><li><p>IoTDB supports <strong>adhoc</strong> downsampling data in <strong>arbitrary time</strong>. |
| e.g., a SQL returns 1 point per 5 minutes and start with 2020-04-27 08:00:00 while another SQL returns 1 point per 5 minutes + 10 seconds and start with 2020-04-27 08:00:01. |
| (InfluxDB also supports adhoc downsampling but the performance is ..... hm)</p></li> <li><p>There is no disk loss for IoTDB.</p></li></ul></li> <li><p>Fill:</p> <p>Sometimes we thought the data is collected in some fixed frequency, e.g., 1Hz (1 point per second). |
| But usually, we may lost some data points, because the network is unstable, the machine is busy, or the machine is down for several minutes.</p> <p>In this case, filling these holes is important. Data scientists can avoid to many so called dirty work, e.g., data clean.</p> <p>InfluxDB and OpenTSDB only support using fill in a group by statement, while IoTDB supports to fill data when just given a particular timestamp. |
| Besides, IoTDB supports several strategies for filling data.</p></li> <li><p>Slimit:</p> <p>Slimit means return limited number of measurements (or, fields in InfluxDB). |
| For example, a wind turbine may have 1000 measurements (speed, voltage, etc..), using slimit and soffset can just return a part of them.</p></li> <li><p>Latest value:</p> <p>As one of the most basic timeseries based applications is monitoring the latest data. |
| Therefore, a query to return the latest value of a time series is very important. |
| IoTDB and OpenTSDB support that with a special SQL or API, |
| while InfluxDB supports that using an aggregation function. |
| (the reason why IoTDB provides a special SQL is IoTDB optimizes the query expressly.)</p></li></ul> <p><strong>Conclusion</strong>:</p> <p>Well, if we compare the basic features, we can find that OpenTSDB and KairosDB somehow lack some important query features. |
| TimescaleDB can not be freely used in business. |
| IoTDB and InfluxDB can meet most requirements of time series data management, while they have some difference.</p> <h4 id="advanced-features"><a href="#advanced-features" class="header-anchor">#</a> Advanced Features</h4> <p>I listed some interesting features that these systems may differ.</p> <table><thead><tr><th>TSDB</th> <th>IoTDB</th> <th>InfluxDB</th> <th>OpenTSDB</th> <th>KairosDB</th> <th>TimescaleDB</th></tr></thead> <tbody><tr><td>Align by time</td> <td><strong>O</strong></td> <td>o</td> <td>x</td> <td>x</td> <td>o</td></tr> <tr><td>Compression</td> <td><strong>O</strong></td> <td>😦</td> <td>😦</td> <td>😦</td> <td>😦</td></tr> <tr><td>MQTT support</td> <td><strong>O</strong></td> <td>o</td> <td>x</td> <td>x</td> <td>😦</td></tr> <tr><td>Run on Edge-side Device</td> <td><strong>O</strong></td> <td>o</td> <td>x</td> <td>😦</td> <td>o</td></tr> <tr><td>Multi-instance Sync</td> <td><strong>O</strong></td> <td>x</td> <td>x</td> <td>x</td> <td>x</td></tr> <tr><td>JDBC Driver</td> <td><strong>o</strong></td> <td>x</td> <td>x</td> <td>x</td> <td>x</td></tr> <tr><td>Standard SQL</td> <td>o</td> <td>x</td> <td>x</td> <td>x</td> <td><strong>O</strong></td></tr> <tr><td>Spark integration</td> <td><strong>O</strong></td> <td>x</td> <td>x</td> <td>x</td> <td>x</td></tr> <tr><td>Hive integration</td> <td><strong>O</strong></td> <td>x</td> <td>x</td> <td>x</td> <td>x</td></tr> <tr><td>Writing data to NFS (HDFS)</td> <td><strong>O</strong></td> <td>x</td> <td>o</td> <td>x</td> <td>x</td></tr> <tr><td>Flink integration</td> <td><strong>O</strong></td> <td>x</td> <td>x</td> <td>x</td> <td>x</td></tr></tbody></table> <ul><li><p>Align by time: have been introduced. Let's skip it..</p></li> <li><p>Compression:</p> <ul><li>IoTDB supports many encoding and compression for time series, like RLE, 2DIFF, Gorilla, etc.. and Snappy compression. |
| In IoTDB, you can choose which encoding method you want, according to the data distribution. For more info, see <a href="http://iotdb.apache.org/UserGuide/Master/Concept/Encoding.html" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a>.</li> <li>InfluxDB also supports encoding and compression, but you can not define which encoding method you want. |
| It just depends on the data type. For more info, see <a href="https://docs.influxdata.com/influxdb/v1.7/concepts/storage_engine/" target="_blank" rel="noopener noreferrer">here<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a>.</li> <li>OpenTSDB and KairosDB use HBase and Cassandra in backend, and have no special encoding for time series.</li></ul></li> <li><p>MQTT protocol support:</p> <p>MQTT protocol is an international standard and widely known in industrial users. only IoTDB and InfluxDB support user using MQTT client to write data.</p></li> <li><p>Running on Edge-side Device:</p> <p>Nowdays, edge computing is more and more popular, which means the edge device has more powerful compution resources. |
| Deploying a TSDB on the edge side is useful for managing data on the edge side and serve for edge computing. |
| As OpenTSDB and KairosDB rely another DB, the architecture is a little heavy. Especially, it is hard to run Hadoop on the edge side.</p></li> <li><p>Multi-instance Sync:</p> <p>Ok, now we have many TSDB instances on the edge-side. Then, how to upload their data to the data center, to form a ... data lake (or ocean, river,..., whatever). |
| One choice is read data from these instances and write the data point by point to the data center instance. |
| IoTDB provides another choice, just uploading the data file into the data center incrementally, then the data center can support service on the data.</p></li> <li><p>JDBC driver:</p> <p>Now only IoTDB supports a JDBC driver (though not all interfaces are implemented), and makes it possible to integrate many other JDBC driver based softwares.</p></li> <li><p>Standard SQL:</p> <p>As mentioned, the integration of IoTDB and Calcite is almost done (a PR has been submitted), which means IoTDB will support Standard SQL.</p></li> <li><p>Spark and Hive integration:</p> <p>It is very very important that letting big data analysis software to access the data in database for more complex data analysis. |
| IoTDB supports Hive-connector and Spark connector for better integration.</p></li> <li><p>Writing data to NFS (HDFS): |
| Sharing nothing architecture is good, but sometimes you have to add new servers even your CPU and memory is idle but the disk is full... |
| Besides, if we can save the data file directly to HDFS, it will be more easy to use Spark and other softwares to analyze data, without ETL.</p></li> <li><p>IoTDB supports write data locally or on HDFS directly. IoTDB also allows user extend to store data on other NFS.</p></li> <li><p>InfluxDB, KairosDB have to write data locally.</p></li> <li><p>OpenTSDB has to write data on HDFS.</p></li></ul> <p><strong>Conclusion</strong>:</p> <p>We can find that IoTDB has many powerful features that other TSDBs do not support.</p> <h3 id="performance-comparison"><a href="#performance-comparison" class="header-anchor">#</a> Performance Comparison</h3> <p>Ok... If you say, "well, I just want to use the basic features. If so, IoTDB has little difference with others.". |
| It is somehow right. But, if you consider the performance, you may change your mind.</p> <h4 id="quick-review"><a href="#quick-review" class="header-anchor">#</a> quick review</h4> <p>Given a workload:</p> <ul><li>Write:</li></ul> <p>10 clients write data concurrently. The number of storage group is 50. There are 1000 devices and each device has 100 measurements (i.e.,, 100K time series totally). |
| The data type is float and IoTDB uses RLE encoding and Snappy compression. |
| IoTDB uses batch insertion API and the batch size is 100 (write 100 data points per write API call).</p> <ul><li>Read:</li></ul> <p>50 clients read data concurrently. Each client just read data from 1 device with 10 measurements in one storage group.</p> <p>IoTDB is v0.9.0.</p> <p><strong>Write performance</strong>:</p> <p>We write 112GB data totally.</p> <p>The write throughput (points/second) is:</p> <p><img src="https://user-images.githubusercontent.com/1021782/80472896-f1db0e00-8977-11ea-9424-96bf0021588d.png" alt="Write Throughput (points/second)"> <span id="exp1"><center>Figure 1. Write throughput (points/second) IoTDB v0.9</center></span></p> <p>The disk occupation is:</p> <p><img src="https://user-images.githubusercontent.com/1021782/80472899-f3a4d180-8977-11ea-8233-268ad4e3713e.png" alt="Disk Occupation"> <center>Figure 2. Disk occupation(GB) IoTDB v0.9</center></p> <p><strong>Query performance</strong></p> <p><img src="https://user-images.githubusercontent.com/1021782/80472924-fef7fd00-8977-11ea-9ad4-b4d3c899605e.png" alt="Aggregation query"> <center>Figure 3. Aggregation query time cost(ms) IoTDB v0.9</center></p> <p>We can see that IoTDB outperforms others.</p> <h4 id="more-details"><a href="#more-details" class="header-anchor">#</a> More details</h4> <p>We provide a benchmarking tool, called IoTDB-benchamrk (https://github.com/thulab/iotdb-benchmark, you may have to use the dev branch to compile it), |
| it supports IoTDB, InfluxDB, KairosDB, TimescaleDB, OpenTSDB. We have a <a href="https://arxiv.org/abs/1901.08304" target="_blank" rel="noopener noreferrer">article<svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" x="0px" y="0px" viewBox="0 0 100 100" width="15" height="15" class="icon outbound"><path fill="currentColor" d="M18.8,85.1h56l0,0c2.2,0,4-1.8,4-4v-32h-8v28h-48v-48h28v-8h-32l0,0c-2.2,0-4,1.8-4,4v56C14.8,83.3,16.6,85.1,18.8,85.1z"></path> <polygon fill="currentColor" points="45.7,48.7 51.3,54.3 77.2,28.5 77.2,37.2 85.2,37.2 85.2,14.9 62.8,14.9 62.8,22.9 71.5,22.9"></polygon></svg></a> for comparing these systems using the benchmark tool. |
| When we publishing the article, IoTDB just entered Apache incubator, so we deleted the performance of IoTDB in that article. But we really did the comparison, and I will |
| disclose some results here.</p> <ul><li><strong>IoTDB: 0.8.0</strong>. (notice: <strong>IoTDB v0.9 outperforms than v0.8</strong>, we will update the result once we finish the experiments on v0.9)</li> <li>InfluxDB: 1.5.1.</li> <li>OpenTSDB: 2.3.1 (HBase 1.2.8)</li> <li>KairosDB: 1.2.1 (Cassandra 3.11.3)</li> <li>TimescaleDB: 1.0.0 (PostgreSQL 10.5)</li></ul> <p>All TSDB run on the same server one by one.</p> <ul><li><p>For InfluxDB, we set the cache-max-memory-size and max-series-perbase as unlimited (otherwise it will be timeout quickly)</p></li> <li><p>For OpenTSDB, we modified tsd.http.request.enable_chunked, tsd.http.request.max_chunk and tsd.storage.fix_duplicates for supporting write data in batch |
| and write out-of-order data.</p></li> <li><p>For KairosDB, we set Cassandra's read_repair_chance as 0.1 (However it has no effect because we just have one node).</p></li> <li><p>For TimescaleDB, we use PGTune tool to optimize PostgreSQL.</p></li></ul> <p>All TSDBs run on a server with Intel Xeon CPU E5-2697 v4 @2.3GHz, 256GB memory and 10 HDD disks with RAID-5. |
| The OS is Ubuntu 16.04.2 LTS, 64bits.</p> <p>Another server run IoTDB benchmark tool.</p> <p>I omit the detailed workload here, let's see the result:</p> <p>Legend:</p> <ul><li>I: InfluxDB</li> <li>O: OpenTSDB</li> <li>T: TimescaleDB</li> <li>K: KairosDB</li> <li><strong>D: IoTDB</strong></li></ul> <p><img src="https://user-images.githubusercontent.com/1021782/80476160-95c6b880-897c-11ea-9bb3-9d810cc0c79e.png" alt="Write experiments"> <span id="exp4"><center>Figure 4. Write experiments IoTDB v0.8.0</center></span></p> <p><img src="https://user-images.githubusercontent.com/1021782/80476181-9c553000-897c-11ea-8170-4768134f5841.png" alt="Query experiments"> <center>Figure 5. Query experiments IoTDB v0.8.0</center></p> <p>We can see that IoTDB outperforms others hugely.</p> <p>In <a href="#exp4">Figure. 4(c)</a>, when the batch size reaches to 10000 points, InfluxDB is better than IoTDB v0.8. |
| It is because in IoTDB v0.8, batch insert API is not optimized.</p> <p>From IoTDB v0.9 on, using batch insert API can obtain 8 to 10 times write performance improvement.</p> <p>For example, using IoTDB v0.8, the write throughput can only reach to 6 million data points per second. |
| But using IoTDB v0.9, the write throughput can reach to 40 million data points per second on the same server with the same workload. |
| (see <a href="#exp4">Figure. 4(a)</a> vs <a href="#exp1">Figure. 1</a>).</p> <h2 id="conclusion"><a href="#conclusion" class="header-anchor">#</a> Conclusion</h2> <p>If you are considering to find a TSDB for your IIoT application, then Apache IoTDB, a new time series, is your best choice.</p> <p>We will update this page once we release new version and finish the experiments. |
| We also welcome more contributors correct this article and contribute IoTDB and reproduce experiments.</p></div> <div class="page-nav"><p class="inner"><span class="prev"> |
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| Disclaimer: Apache IoTDB (incubating) is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. |
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