Project: ShardingSphere (App Definition and Development / Database)
File: result.en.md

+++ title = "How to merge the child resultsets" weight = 3 chapter = true +++ This series of articles is a hands-on introduction and analysis of SS's core modules, the cutting-edge technologies used, and valuable lessons learned by SS's core development members. This series of articles will take you into the world of SS kernel, to gain new knowledge and inspire. We hope you will pay attention to us, exchange ideas and go forward together. Liang Zhang, former head of Dangdang's architecture department. A passion for open source, currently leading two open source projects Elastic-Job and Sharding-Sphere (Sharding-JDBC). Specializing in java-based distributed architecture and Kubernetes and Mesos-based cloud platform direction, advocate for elegant code, more research on how to write expressive code.Joined BOF in early 2018, now as the head of data research and development. Currently, the main focus is on building Sharding-Sphere into the industry's best-in-class financial-grade data solution. Combining multiple data result sets from various data nodes into one result set and returning it correctly to the requesting client is called result imputation. The result merging supported by Sharding-Sphere is functionally categorized into four types of traversal, sorting, grouping and paging, which are combinations rather than mutually exclusive relationships. Structurally, it can be divided into streaming, in-memory and decorator grouping. Streaming and memory imputation are mutually exclusive, and decorator imputation can be further processed on top of stream imputation and memory imputation. Since the result set returned from the database is returned one by one, it is not necessary to load all the data into memory at once, therefore, when merging the results, merging in the same way as the result set returned from the database can greatly reduce memory consumption, and is the preferred method of merging. Streaming merge means that each time the data is fetched from the result set, the correct individual data can be returned by fetching it one by one, which is most compatible with the way the database returns the result set natively. Traversal, sorting, and Stream Group-by Merger are all types of stream imputation. In-memory merging, on the other hand, requires that all data in the result set be traversed and stored in memory, and then after unified grouping, sorting, and aggregation calculations, it is encapsulated to return the result set of data accessed one item at a time. The decorator merge is a unified functional enhancement of all the result set merge, currently the decorator merge only paging this type of merge. It is the simplest form of aggregation. Simply merge multiple result sets into a one-way chain table. After iterating through the current result set in the chain table, move the chain table element back one place and continue to iterate through the next result set. Due to the existence of the ORDER BY statement in SQL, each data result set is itself ordered, so only the data values that the current cursor of the data result set points to need to be

This is equivalent to sorting multiple ordered arrays, and merge sorting is the most appropriate sorting algorithm for this scenario. Sharding-Sphere compares the current data values of each result set (done by implementing Java's Comparable interface) and places them in a priority queue when the sorted queries are merged. Each time the next piece of data is fetched, simply move the cursor of the result set at the top of the queue down and find your place in the priority queue again based on the new cursor. To illustrate Sharding-Sphere's sorting and merging with an example, the following diagram shows an example of sorting by score. The example shows 3 tables returning data result sets, each of which is already sorted according to score, but is unordered between the 3 data result sets. The data values pointed to by the current cursor for each of the 3 result sets are sorted and placed into a priority queue, with the first data value of tscore0 being the largest, the first data value of tscore2 the next largest, and the first data value of tscore1 the smallest, so the priority queue is based on the tscore0, tscore2, and tscore1's The following diagram shows how sorting the queue is done when the next call is made. The following diagram shows how the sorting and merging is done when the next call is made. As we can see from the diagram, when the first NEXT call is made, tscore0, which is at the top of the queue, is ejected from the queue and returns the data value (i.e., 100) that the current cursor points to to the querying client, and is placed back into the priority queue after moving the cursor down one place. The priority queue is also ranked according to the data value (in this case 90) that the current result set of tscore0 points to for the cursor, with tscore0 ranked last in the queue according to the current value. The result set of tscore2, which was previously ranked second in the queue, is automatically ranked first in the queue. For a second NEXT, simply eject the tscore2 currently at the top of the queue and return the value pointed to by its data result set cursor to the client, and move the cursor down to continue to join the queue for queuing, and so on. When there is no more data in a result set, there is no need to join the queue again. As you can see, when the data in each result set is ordered, but the whole result set is disordered, Sharding-Sphere can sort the data without adding all the data to memory, it uses stream merge, each time only one correct data is obtained by next, which greatly saves memory consumption. From another perspective, Sharding-Sphere's sorting and merging is maintaining the orderliness of the two dimensions of the data result set, the vertical axis and the horizontal axis. The vertical axis refers to each data result set itself, which is naturally ordered and is fetched by the SQL containing ORDER

The horizontal axis refers to the values pointed to by the current cursor of each result set, which need to be maintained in the correct order by the priority queue. Each time the current cursor of the result set is moved down, the result set is reordered into the priority queue, and only the result set at the top of the queue can have a cursor move down. Grouping by Merger is the most complex, and it is divided into Stream Group-by Merger and in-memory grouping. Stream Group-by Merger requires that the fields and sort type (ASC or DESC) of SQL's sorted and grouped items must be identical, otherwise only in-memory grouping can ensure the correctness of its data. To illustrate, suppose that the table structure contains the candidate's name (disregarding renames for simplicity) and score based on subject slicing. The total score for each candidate can be obtained via SQL as follows. In the case where the grouping item is identical to the sorting item, the data obtained is continuous, and the full amount of data required for grouping exists in the data values pointed to by the current cursor of each data result set, so streamwise merging can be used. This is shown in the figure below. When performing a merge, the logic is similar to a sorted merge. The following diagram shows how stream group merging is performed when next call is made. As you can see in the diagram, when the first next call is made, the first tscorejava is ejected from the queue, along with all other result sets with the same value as "Jetty". After obtaining the scores of all students named "Jetty" and adding them up, the result set is the sum of "Jetty's" scores at the end of the first next call. At the same time, all cursors in the result set are moved down to the next different value of "Jetty" and re-sorted according to the value of the current cursor in the result set. As a result, the relevant result set containing the name "John" in the second place is at the top of the queue. Stream Group-by Merger merging differs from sorted merging in just two ways. It will be a one-time multiple data result set of grouped items of the same data out of all. It needs to be aggregated according to the type of aggregation function to calculate. For cases where the grouped item does not match the sorted item, since the data values associated with the grouping that needs to be obtained are not continuous, stream merging cannot be used and all the result set data needs to be loaded into memory for grouping and aggregation. For example, if the following SQL is used to obtain the total score for each candidate and sort the scores from highest to

Then the data taken out of each data result set is consistent with the original data in the table structure in the upper half of the sorting example diagram for the scores, and it is not possible to perform streamwise summation. When the SQL contains only grouping statements, the order of sorting may not be the same as the grouping order depending on the implementation of different databases. However, the absence of the sorting statement means that this SQL does not care about the sorting order. Therefore, Sharding-Sphere automatically adds sorted items consistent with grouped items through SQL-optimized rewrites, allowing it to convert from a memory-consuming in-memory grouped imputation approach to a streaming grouped imputation scheme. The handling of aggregation functions is the same for both stream grouping and in-memory grouping. Aggregation functions can be categorized into three types: comparison, accumulation and averaging. Comparison of the type of aggregate function refers to MAX and MIN. they need to be compared with each group of the results of the set of data, and directly return the maximum or minimum value can be. Sum and COUNT are aggregation functions that need to be added to the result set for each cohort. It must be calculated by the SQL rewrite of SUM and COUNT, the relevant content has been covered in the SQL rewrite, not to repeat. Pagination is possible for all of the merge types described above. Pagination is a decorator appended on top of other merge types, and Sharding-Sphere adds the ability to paginate the result set of data through the decorator mode. The Pagination imputation is responsible for filtering out data that does not need to be fetched. Sharding-Sphere's Pagination capabilities are rather misleading to users, who often think that paged merging takes up a lot of memory. In a distributed scenario, it is necessary to rewrite LIMIT 10000000, 10 to LIMIT 0, 10000010 in order to ensure that their data is correct. It is very easy for users to create the illusion that Sharding-Sphere will load large amounts of meaningless data into memory, creating a risk of memory overflow. In fact, through the principle of stream merge, we can see that the only case that will load all the data into memory is the memory group merge, and generally speaking, OLAP group SQL will not produce a large number of result data, it is more used for a large number of calculations, and a small number of results output scenarios. Except for the case of in-memory group merge, all other cases get the result set of data via stream merge, so Sharding-Sphere skips all the data that doesn't need to be taken out via the result set's NEXT method and doesn't store it in memory. However, it is also important to note that a large amount of data still needs to be transferred to Sharding-Sphere's memory space due to the sorting needs. Therefore, it is not a best practice to use LIMIT for Pagination in this manner. Since LIMIT does not query data by index, Pagination by ID is a better solution if continuity of ID can be guaranteed, e.g.. Or by recording the ID of the last record of the last query result for the next page, for example. The whole structure of merger engine is showing below: