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According to the characteristics of time series data, IoTDB partitions them by series and time dimensions. Combining a series partition with a time partition creates a partition, the unit of division. To enhance throughput and reduce management costs, these partitions are evenly allocated to RegionGroups, which serve as the unit of replication. The RegionGroup's Regions then determine the storage location, with the leader Region managing the primary load. During this process, the Region placement strategy determines which nodes will host the replicas, while the leader selection strategy designates which Region will act as the leader.</p><h2 id="partitioning-strategy-partition-allocation" tabindex="-1"><a class="header-anchor" href="#partitioning-strategy-partition-allocation"><span>Partitioning Strategy & Partition Allocation</span></a></h2><p>IoTDB implements tailored partitioning algorithms for time series data. Building on this foundation, the partition information cached on both ConfigNodes and DataNodes is not only manageable in size but also clearly differentiated between hot and cold. Subsequently, balanced partitions are evenly allocated across the cluster's RegionGroups to achieve storage balance.</p><h3 id="partitioning-strategy" tabindex="-1"><a class="header-anchor" href="#partitioning-strategy"><span>Partitioning Strategy</span></a></h3><p>IoTDB maps each sensor in the production environment to a time series. The time series are then partitioned using the series partitioning algorithm to manage their schema, and combined with the time partitioning algorithm to manage their data. The following figure illustrates how IoTDB partitions time series data.</p><img style="width:100%;max-width:800px;max-height:600px;margin-left:auto;margin-right:auto;display:block;" src="'+r+'"><h4 id="partitioning-algorithm" tabindex="-1"><a class="header-anchor" href="#partitioning-algorithm"><span>Partitioning Algorithm</span></a></h4><p>Because numerous devices and sensors are commonly deployed in production environments, IoTDB employs a series partitioning algorithm to ensure the size of partition information is manageable. Since the generated time series associated with timestamps, IoTDB uses a time partioning algorithm to clearly distinguish between hot and cold partitions.</p><h5 id="series-partitioning-algorithm" tabindex="-1"><a class="header-anchor" href="#series-partitioning-algorithm"><span>Series Partitioning Algorithm</span></a></h5><p>By default, IoTDB limits the number of series partitions to 1000 and configures the series partitioning algorithm to use a hash partitioning algorithm. This leads to the following outcomes:</p><ul><li>Since the number of series partitions is a fixed constant, the mapping between series and series partitions remains stable. As a result, IoTDB does not require frequent data migrations.</li><li>The load across series partitions is relatively balanced because the number of series partitions is much smaller than the number of sensors deployed in the production environment.</li></ul><p>Furthermore, if a more accurate estimate of the actual load in the production environment is available, the series partitioning algorithm can be configured to use a customized hash partitioning or a list partitioning to achieve a more uniform load distribution across all series partitions.</p><h5 id="time-partitioning-algorithm" tabindex="-1"><a class="header-anchor" href="#time-partitioning-algorithm"><span>Time Partitioning Algorithm</span></a></h5><p>The time partitioning algorithm converts a given timestamp to the corresponding time partition by</p>',15)),Q("mjx-container",V,[(n(),e("svg",d,T[0]||(T[0]=[a('<g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g 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corresponds to a schema partition. These schema partitions are then evenly allocated across the SchemaRegionGroups to achieve a balanced schema distribution.</p><h4 id="data-partitioning" tabindex="-1"><a class="header-anchor" href="#data-partitioning"><span>Data Partitioning</span></a></h4><p>Combining a series partition with a time partition creates a data partition. Since the series partitioning algorithm evenly partitions the time series, the load of data partitions within a specified time partition remains balanced. These data partitions are then evenly allocated across the DataRegionGroups to achieve balanced data distribution.</p><h3 id="partition-allocation" tabindex="-1"><a class="header-anchor" href="#partition-allocation"><span>Partition Allocation</span></a></h3><p>IoTDB uses RegionGroups to enable elastic storage of time series, with the number of RegionGroups in the cluster determined by the total resources available across all DataNodes. Since the number of RegionGroups is dynamic, IoTDB can easily scale out. Both the SchemaRegionGroup and DataRegionGroup follow the same partition allocation algorithm, which evenly splits all series partitions. The following figure demonstrates the partition allocation process, where the dynamic RegionGroups match the variously expending time series and cluster.</p><img style="width:100%;max-width:800px;max-height:600px;margin-left:auto;margin-right:auto;display:block;" src="'+s+'"><h4 id="regiongroup-expansion" tabindex="-1"><a class="header-anchor" href="#regiongroup-expansion"><span>RegionGroup Expansion</span></a></h4><p>The number of RegionGroups is given by</p>',9)),Q("mjx-container",x,[(n(),e("svg",Z,T[18]||(T[18]=[a('<g stroke="currentColor" fill="currentColor" stroke-width="0" transform="scale(1,-1)"><g data-mml-node="math"><g data-mml-node="mtext"><path data-c="52" d="M130 622Q123 629 119 631T103 634T60 637H27V683H202H236H300Q376 683 417 677T500 648Q595 600 609 517Q610 512 610 501Q610 468 594 439T556 392T511 361T472 343L456 338Q459 335 467 332Q497 316 516 298T545 254T559 211T568 155T578 94Q588 46 602 31T640 16H645Q660 16 674 32T692 87Q692 98 696 101T712 105T728 103T732 90Q732 59 716 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As a result, each SchemaRegionGroup holds the same number of schema partitions, ensuring balanced schema storage. Similarly, for each time partition, each DataRegionGroup acquires the data partitions corresponding to the series partitions it holds. Consequently, the data partitions within a time partition are evenly distributed across all DataRegionGroups, ensuring balanced data storage in each time partition.</p><p>Notably, IoTDB effectively leverages the characteristics of time series data. When the TTL (Time to Live) is configured, IoTDB enables migration-free elastic storage for time series data. This feature facilitates cluster expansion while minimizing the impact on online operations. The figures above illustrate an instance of this feature: newborn data partitions are evenly allocated to each DataRegion, and expired data are automatically archived. As a result, the cluster's storage will eventually remain balanced.</p><h2 id="balance-strategy" tabindex="-1"><a class="header-anchor" href="#balance-strategy"><span>Balance Strategy</span></a></h2><p>To enhance the cluster's availability and performance, IoTDB employs sophisticated storage load and computing load balance algorithms.</p><h3 id="storage-load-balance" tabindex="-1"><a class="header-anchor" href="#storage-load-balance"><span>Storage Load Balance</span></a></h3><p>The number of Regions held by a DataNode reflects its storage load. If the difference in the number of Regions across DataNodes is relatively large, the DataNode with more Regions is likely to become a storage bottleneck. 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The rationale is that each greedy step lacks a global perspective, leading to a locally optimal solution."))]),T[142]||(T[142]=a('<p>To address this issue, IoTDB employs a leader selection algorithm that can consistently balance the cluster's leader distribution. Consequently, the cluster achieves balanced Computing load distribution, ensuring its performance.</p><h2 id="source-code" tabindex="-1"><a class="header-anchor" href="#source-code"><span>Source Code</span></a></h2><ul><li><a href="https://github.com/apache/iotdb/tree/master/iotdb-core/node-commons/src/main/java/org/apache/iotdb/commons/partition" target="_blank" rel="noopener noreferrer">Data Partitioning</a></li><li><a href="https://github.com/apache/iotdb/tree/master/iotdb-core/confignode/src/main/java/org/apache/iotdb/confignode/manager/load/balancer/partition" target="_blank" rel="noopener noreferrer">Partition Allocation</a></li><li><a href="https://github.com/apache/iotdb/tree/master/iotdb-core/confignode/src/main/java/org/apache/iotdb/confignode/manager/load/balancer/region" target="_blank" rel="noopener noreferrer">Region Placement</a></li><li><a href="https://github.com/apache/iotdb/tree/master/iotdb-core/confignode/src/main/java/org/apache/iotdb/confignode/manager/load/balancer/router/leader" target="_blank" rel="noopener noreferrer">Leader Selection</a></li></ul>',3))])}const z1=i(H,[["render",E1],["__file","Cluster-data-partitioning.html.vue"]]),q1=JSON.parse('{"path":"/UserGuide/V1.3.x/Technical-Insider/Cluster-data-partitioning.html","title":"Load Balance","lang":"en-US","frontmatter":{"description":"Load Balance This document introduces the partitioning strategies and load balance strategies in IoTDB. 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