From 69dc1a18e646aaf3cc9872cae825ae1c3fc5fde6 Mon Sep 17 00:00:00 2001 From: Yingchun Lai Date: Fri, 12 Jan 2024 12:15:59 +0800 Subject: [PATCH] Update GEO docs (#58) --- _docs/en/api/geo.md | 327 +++++++++++++++++++++++++++++++++++++++++- _docs/zh/api/geo.md | 83 +++++------ _docs/zh/api/redis.md | 2 +- 3 files changed, 369 insertions(+), 43 deletions(-) diff --git a/_docs/en/api/geo.md b/_docs/en/api/geo.md index bb36c294..0078e6bc 100755 --- a/_docs/en/api/geo.md +++ b/_docs/en/api/geo.md @@ -2,4 +2,329 @@ permalink: api/geo --- -TRANSLATING +# Pegasus GEO + +## Background + +In Pegasus, if the user data is POI (Points of Interest) data, which contains geographic information, such as longitude and latitude in value, and users require Pegasus to provide interfaces to support GEO features. For example, given a center point coordinate and a radius, search for all data within this range. Given the hashkey and sortkey of two POI data, calculate the geographical distance between these two pieces of data. + +Pegasus's GEO (Geographic) support uses [S2](https://github.com/google/s2geometry) library, mainly used for converting two-dimensional geographic coordinates (longitude+latitude) to one-dimensional encoding, range queries based on circles, Hilbert curve rules, and other characteristics. + +This article will explain how to fully utilize the characteristics of S2 in Pegasus, and combine the data distribution and storage characteristics of Pegasus to support GEO features. + +Please refer to the [S2 official website](http://s2geometry.io/) for the implementation principle of S2. + +## Coordinate transformation + +In S2, two-dimensional longitude and latitude can be encoded into one-dimensional encoding, which consists of two parts: face cells and plane coordinate encoding, such as: + +The encoding of two-dimensional coordinate (116.334441, 40.030202) is: `1/223320022232200331010110113301`(32 bytes), it is called **CellId** in S2. + +- The first `1` represents the face cell index of the Earth cube projection, with an index range of 0~5, as shown in the following figure: + +![geo_faces.png](/assets/images/geo_faces.png){:class="img-responsive"} +- `/` is a delimiter +- `223320022232200331010110113301`(30 bytes), is the encoding obtained through a series of transformations of latitude and longitude coordinates, and the specific transformation process is not described in detail here. It should be pointed out that this is a Hilbert curve encoding, which is characterized by stability and continuity. + +![hilbert.png](/assets/images/hilbert.png){:class="img-responsive"} + +Hilbert curve encoding in S2: +- Encoding can be seen as a 4-digit numerical encoding +- Encoding is done level by level from left to right, with a maximum of 30 levels +- A code represents a geographic block area, and the longer the code, the smaller the area +- The complete encoding is a sub-region of its prefix encoding, with each parent region consisting of four sub-regions. For example, `00`, `01`, `02`, and `03` are sub-regions of `0`, and the union of the sub-regions equal to the region of the prarent's. +- Numerically continuous values are also geographically adjacent, for example, the range of regions for `00` and `01` is adjacent, and the range of regions for `0122` and `0123` is also adjacent + +## Encoding accuracy + +The Hilbert curve encoding in S2 consists of 30 bytes, each representing a level partition. The following table shows the area and number of individual cells in each level. + +| **level** | **min area** | **max area** | **average area** | **units** | **Number of cells** | +|-----------|--------------|--------------|------------------|-----------|---------------------| +| 00 | 85011012.19 | 85011012.19 | 85011012.19 | km^2 | 6 | +| 01 | 21252753.05 | 21252753.05 | 21252753.05 | km^2 | 24 | +| 02 | 4919708.23 | 6026521.16 | 5313188.26 | km^2 | 96 | +| 03 | 1055377.48 | 1646455.50 | 1328297.07 | km^2 | 384 | +| 04 | 231564.06 | 413918.15 | 332074.27 | km^2 | 1536 | +| 05 | 53798.67 | 104297.91 | 83018.57 | km^2 | 6K | +| 06 | 12948.81 | 26113.30 | 20754.64 | km^2 | 24K | +| 07 | 3175.44 | 6529.09 | 5188.66 | km^2 | 98K | +| 08 | 786.20 | 1632.45 | 1297.17 | km^2 | 393K | +| 09 | 195.59 | 408.12 | 324.29 | km^2 | 1573K | +| 10 | 48.78 | 102.03 | 81.07 | km^2 | 6M | +| 11 | 12.18 | 25.51 | 20.27 | km^2 | 25M | +| 12 | 3.04 | 6.38 | 5.07 | km^2 | 100M | +| 13 | 0.76 | 1.59 | 1.27 | km^2 | 402M | +| 14 | 0.19 | 0.40 | 0.32 | km^2 | 1610M | +| 15 | 47520.30 | 99638.93 | 79172.67 | m^2 | 6B | +| 16 | 11880.08 | 24909.73 | 19793.17 | m^2 | 25B | +| 17 | 2970.02 | 6227.43 | 4948.29 | m^2 | 103B | +| 18 | 742.50 | 1556.86 | 1237.07 | m^2 | 412B | +| 19 | 185.63 | 389.21 | 309.27 | m^2 | 1649B | +| 20 | 46.41 | 97.30 | 77.32 | m^2 | 7T | +| 21 | 11.60 | 24.33 | 19.33 | m^2 | 26T | +| 22 | 2.90 | 6.08 | 4.83 | m^2 | 105T | +| 23 | 0.73 | 1.52 | 1.21 | m^2 | 422T | +| 24 | 0.18 | 0.38 | 0.30 | m^2 | 1689T | +| 25 | 453.19 | 950.23 | 755.05 | cm^2 | 7e15 | +| 26 | 113.30 | 237.56 | 188.76 | cm^2 | 27e15 | +| 27 | 28.32 | 59.39 | 47.19 | cm^2 | 108e15 | +| 28 | 7.08 | 14.85 | 11.80 | cm^2 | 432e15 | +| 29 | 1.77 | 3.71 | 2.95 | cm^2 | 1729e15 | +| 30 | 0.44 | 0.93 | 0.74 | cm^2 | 7e18 | + + +## Data Storage + +In Pegasus, the key for data storage is combined by hashkey and sortkey: hashkey is used to determine the partition where the data is located. Data belongs to the same hashkey is stored in a logically contiguous area in the same Replica Server, and sortkey is used to sort the data in this area. + +After converting the longitude and latitude coordinates to obtain the one-dimensional encoding `CellId`, this one-dimensional encoding can be stored as a key for **GEO index data**. Pegasus divides this one-dimensional encoding into two parts: hashkey and sortkey, and different byte partitioning strategies can be adopted according to actual user scenarios. + +GEO index data is independent of the original data, and the two types of data are stored in different Pegasus tables. Pegasus uses [GEO Client](https://github.com/apache/incubator-pegasus/blob/master/src/geo/lib/geo_client.h) to synchronize data for the two tables, and supports access to both native Pegasus API and GEO API. + +So, when using the Pegasus GEO feature, it is necessary to create two Pegasus tables: one is the original table to store the raw data written by the user, and the other is the GEO index table to store the GEO index data generated by the GEO client's automatic conversion of raw data. + +### hashkey + +Hashkey is composed of one-dimensional encoded prefixe. For example, in a user scenario, setting the hashkey length to `14` (1-byte face, 1-byte delimiter `/`, 12 byte Hilbert encoding) can achieve better performance. + +> So, the **minimum search level** is 12 + +``` + CellId +|1/223320022232..................| +|-------------32 bytes-----------| +|---14 bytes--| + hashkey +``` + +### sortkey + +To meet the requirements of queries with different radius ranges and precisions, we put all the remaining 18 bytes of CellId into the sortkey. +- If the query is over larger radius ranges, take fewer sortkey bytes (corresponding to shorter CellId) as prefixes for data scan queries, which can reduce the number of data scans +- If the query is over smaller radius ranges or point queries, take more sortkey bytes (corresponding to longer CellId) as prefixes for data scan queries, which can reduce the range of data scans + +This can maintain high flexibility for the application without modifying the underlying stored data. + +> When querying data within the same geographical area (such as a circular area), using shorter CellIds to query data has larger ranges and fewer queries, but yields more useless data outside the area. Using longer CellIds to query data results in smaller range of queries, resulting in less useless data outside the region, but with higher number of queries +> +> refer to: [S2 coverings](http://s2geometry.io/devguide/examples/coverings) + +Although the area of the cell is already small enough ( < 1cm^2) at the 30th level, it is still possible for two POI data to fall into the same cell, so it is necessary to solve the key conflict problem based on CellId encoding. Pegasus combines the hashkey and sortkey of the original table and appends them to the sortkey of the GEO data table. + +``` + CellId +|1/223320022232200331010110113301| +|-------------32 bytes-----------| +|---14 bytes--||-----18 bytes----||--原始hashkey--||--原始sortkey--| +|-GEO hashkey-||-------------------GEO sortkey-------------------| +``` + +### value + +When using the Pegasus GEO feature, the value must be able to extract longitude and latitude, and the extract method can be found in [Value Extractor](/api/geo#value_extrator). + +The value of the GEO index table is exactly the same as the value of the original table, so there will be redundant data. Here, trades space for time to avoid secondary indexing. + +> If there is a need to store large data in a single POI data and you want to save disk space, you can manually implement secondary indexing, which means storing the key of the secondary index in the GEO value and then storing the actual large value in another table. + +## Data updates + +### set + +`set` operation will simultaneously update the data of the two tables mentioned above, namely the Pegasus raw table data and GEO index table data, and the data generation method is also described above. + +The hashkey and sortkey of the `set` operation are in the user's own format and are not constrained when using the GEO APIs. The data synchronization of two tables is transparent to users and is automatically completed by the GEO client. + +When using the Redis GEO API, refer to [GEO API](/api/redis#geo-api)。 + +In the Pegasus implementation, the `set` operation first attempts to read and retrieve existing data. If the data does not exist, it directly writes data to both tables. If the data already exists, the old GEO index data will be cleaned up before writing new data. Because the index data `` of new and old data may be different (i.e., the longitudes and latitudes obtained by the extractor for new and old values are different). If not cleaned up, there will be garbage and dirty data in the GEO index table, causing waste of disk space and dirty data will also be found during geographic range queries (i.e. `GEORADIUS`). + +### del + +The `del` operation will delete data from both tables simultaneously, following the same principle above. + +## Data queries + +### Design + +Geographic range queries will be converted into multiple scan operations by Pegasus, with each scan corresponding to all data scans within a CellId range. To achieve higher performance, it is necessary to reduce the total number of scan operations and the amount of data per scan operation, which means reducing the total number of CellIds and the area of a single CellId. + +For example, when performing a range query with the red circle, the CellId query set with a blue blocks can be used as: + +![s2_cap_1.png](/assets/images/s2_cap_1.png){:class="img-responsive"} + +Although such results are more accurate, but there are more CellIds involved in the calculation, resulting in more client-server RPCs, higher network overhead, and higher latency. In addition, in real usage scenarios, CellId that is too small may not have POI data, but it will still consume one RPC. + +So, in the current Pegasus implementation, only two levels of cells, the `minimum search level` and the `maximum search level`, are used together. Taking levels 12 and 16 as examples, the CellId query set obtained is shown in the blue blocks as: + +![s2_cap_2.png](/assets/images/s2_cap_2.png){:class="img-responsive"} + +### 查询流程 + +Taking `search_radial` as an example, it queries all POI data within the circular area according to the given center point and radius. + +> Here we only discuss POI data queries for circular regions, while the idea for other regions such as polygonal regions is similar. + +Use the S2 API to query the CellId set that covers the given region: + +``` +// Returns an S2CellUnion that covers the given region and satisfies the current options. +S2CellUnion GetCovering(const S2Region& region); +``` + +> `search_radial` API 有两个重载函数，一个是输入经纬度，一个是输入 hashky + sortkey，后者是通过 key 取到 value 中的经纬度再转调前者。 + +Query process: + +1. Calculate the circular region S2Cap `C` based on longitude, latitude, and radius +2. Based on the circular region and the specified `minimum search level`, calculate the CellId set on the `minimum search level` using `GetCovering` +3. Traverse these CellIds to determine the relationship between the CellId region and the circular region `C` + 1. Full coverage: Retrieve all POI data within the CellId + 2. Half coverage: Split the CellId according to the `maximum search level` and determine the relationship between sub_CellId region and the circular region `C` + 1. Overlay/Intersection: Take all the POI data in the sub_CellId + 2. Disjoint: Discard + +> `The configuration of the `minimum search level` and the `maximum search level` is referred to in the following documents +> The CellId length of the `minimum search level` determines the hashkey length of the data in GEO index table. + +When querying all the POI data of a CellId, a pair of `start_sortkey` and `stop_sortkey` will be constructed which contain all the POI data of the CellId according to the key construction rules in the previous documents, then use Pegasus' `scan` interface to query data. + +- For the `minimum search level` CellId encoding obtain in step `3.1`, it is also the hashkey of the data in GEO index table, then call Pegasus `scan(CellId, "", "")` to query all POI data + - For example, a cell in level 12, `1/223320022232` is full covered by the region, then call `scan("1/223320022232", "", "")` to query all POI data +- For the sub_CellId set obtain in step `3.2.1`, the hashkey is their prefix, call `scan(sub_CellId_common_prefix, sub_CellId1, sub_CellId2)` to query POI data + - sub_CellId_common_prefix is the common prefix of the CellIds in sub_CellId set, its length is the length of hashkey. All the CellIds between sub_CellId1 and sub_CellId2 are continuous and all are in sub_CellId set, their length is the length of (`maximum search level` - `minimum search level`) + - For example, when the sub-regions `0001`,`0002`,`0003` and `0100` of a cell in level 12 `1/223320022232` are intersect with the search region, then call `scan("1/223320022232", "0001", "0003")`、`scan("1/223320022232", "0100", "0100")` + +After obtaining the result of `scan`, further processing is required: + +- Calculate distance: Because CellId may only partially overlap with the search region, if the POI is outside the search region, discarded it +- Sorting: When there is a requirement for ascending/descending order + +### Flexibility + +Due to storing the complete 30 levels of CellIds, in practical use, we can adjust the `maximum search level` according to the geographic data density, network IO, disk IO conditions. + +> `maximum search level` is 16 by default. + +#### API method + +``` +dsn::error_s set_max_level(int level); +``` + +#### Configuration file method + +``` +[geo_client.lib] +max_level = 16 +``` + +### Invariance + +Due to the fact that the `minimum search level` determines the length of the hashkey in GEO index table, once the data is written to Pegasus, the `minimum search level` cannot be modified because the data has been persisted according to this hashkey length rule. + +The data needs to be reconstructed if modification is required. + +> `minimum search level` is 12 by default. + +``` +[geo_client.lib] +;NOTE: 'min_level' is immutable after some data has been inserted into DB by geo_client. +min_level = 12 +``` + +## Value extractor + +Currently, Pegasus supports extract longitude and latitude from fixed format values. Longitude and latitude are serialized as strings in value, separated by `|`. + +For example: value can be `.*|115.886447|41.269031|.*`, the index of longitude and latitude in value is determined by the `latitude_index` and `longitude_index`. + +## API & Redis Proxy + +There are two ways to use Pegasus GEO features: one is to directly use C++ GEO Client, and the other is to use Redis Proxy. + +[C++ GEO client codebase](https://github.com/apache/incubator-pegasus/blob/master/src/geo/lib/geo_client.h), there is a detailed API description. + +## Configuration + +Please refer to the usage of Redis Proxy [Redis Adaption](redis). + +The configuration files added by GEO feature are as follows: + +``` +[geo_client.lib] +;NOTE: 'min_level' is immutable after some data has been inserted into DB by geo_client. +min_level = 12 +max_level = 16 + +; Used by 'value extractor' +latitude_index = 5 +longitude_index = 4 +``` + +## Data import in batch + +In some usage scenarios, users already has a raw data table which the values contain longitudes and latitudes, then requires constructing the GEO index table mentioned above. The [copy_data](/docs/tools/shell/#copy_data) function in the shell tool to achieve this. For example: + +Before the `copy_data` operation, the target cluster and two target tables (i.e., the raw data table `temp` and GEO index table `temp_geo`) are needed to be created at first. + +``` +copy_data -c target_cluster -a temp -g +``` + +After the data import is completed, Redis Proxy can be set up, please refer to [Redis Adaption](redis). For specific instructions: + +``` +[apps.proxy] +; if using GEO APIs, an extra table name which store geo index data should be appened, i.e. +arguments = redis_cluster temp temp_geo +``` + +## Benchmark + +### Environment + +#### Hardware + +- CPU: E5-2620v3 * 2 +- Memory: 128GB +- Disk: capacity 480GB SSD * 8 +- Network card: bandwidth 1Gb + +#### Cluster + +- Replica Server count：5 +- Version: v1.9.2 +- Partition count of the test table：128 +- Single data size: 120 bytes + +#### Testing interface + +``` +void async_search_radial(double lat_degrees, + double lng_degrees, + double radius_m, + int count, + SortType sort_type, + int timeout_ms, + geo_search_callback_t &&callback); +``` + +**Parameters** +- lat_degrees, lng_degrees: Select random points within the 5th-Ring Road of Beijing every query +- radius_m: The first column of the following table, in meters +- count: -1, which indicates an unlimited number of results +- sort_type: un-ordered + +### Result + +| Radius(m) | P50(ms) | P75(ms) | P99(ms) | P99.9(ms) | Avg result count | QPS per node | +|-----------|-------------|-------------|-------------|-------------|------------------|--------------| +| 50 | 1.63071622 | 1.84607433 | 4.04545455 | 6.28 | 9.4608 | 740.287 | +| 100 | 1.76 | 2.33614794 | 5.4 | 6.45319149 | 38.0296 | 656.66 | +| 200 | 2.41017042 | 3.31062092 | 6.41781609 | 9.60588235 | 154.3682 | 536.624 | +| 300 | 3.30833333 | 4.21979167 | 9.4310559 | 18 | 350.9676 | 434.491 | +| 500 | 5.07763975 | 6.84964682 | 16.84931507 | 21.78082192 | 986.0826 | 347.231 | +| 1000 | 12.28164727 | 18.70972532 | 43.18181818 | 57.049698 | 3947.5294 | 204.23 | +| 2000 | 35.78666667 | 54.7300885 | 108.7331378 | 148.616578 | 15674.1198 | 98.7633 | diff --git a/_docs/zh/api/geo.md b/_docs/zh/api/geo.md index 0f08a32e..566b398d 100755 --- a/_docs/zh/api/geo.md +++ b/_docs/zh/api/geo.md @@ -2,11 +2,11 @@ permalink: api/geo --- -# Pegasus GEO 支持 +# Pegasus GEO ## 背景 -用户数据存储在 Pegasus 中，由 hashkey、sortkey、value 组成。但用户数据中含有地理信息，比如 value 中包含有经纬度，需要 Pegasus 提供 API 进行 GEO 特性的支持。比如给定一个中心点坐标和一个半径，查找这个范围内的所有数据；给定两条数据的 hashkey 和 sortkey，求这两条数据地理上的距离等。 +在 Pegasus 中，当用户数据属于 POI (Points of Interest) 数据，其中含有地理信息，比如 value 中包含有经纬度，需要 Pegasus 提供接口进行 GEO 特性的支持。比如给定一个中心点坐标和一个半径，查找这个范围内的所有数据；给定两条 POI 数据的 hashkey 和 sortkey，求这两条数据地理上的距离等。 Pegasus 的 GEO (Geographic) 支持使用了 [S2](https://github.com/google/s2geometry) 库, 主要用于将二维地理坐标（经度 + 纬度）与一维编码的相互转换、基于圆形的范围查询、Hilbert 曲线规则等特性。 @@ -30,15 +30,15 @@ Pegasus 的 GEO (Geographic) 支持使用了 [S2](https://github.com/google/s2ge ![hilbert.png](/assets/images/hilbert.png){:class="img-responsive"} S2 中的 Hilbert 曲线编码： -- 编码可以看作是一个4进制的数值编码 -- 编码由左往右按层进行，最多30层 +- 编码可以看作是一个 4 进制的数值编码 +- 编码由左往右按层进行，最多 30 层 - 一个编码代表地理上的一个方块区域，编码越长，区域越小 - 完整编码是前缀编码的子区域，每个父区域由4个子区域组成，比如`00`,`01`,`02`,`03`是`0`的子区域，且前者的区域范围的并集就是后者的区域范围 - 在数值上连续的值，在地理位置上也是连续的，比如`00`和`01`的区域范围是相邻的，`0122`和`0123`的区域范围也是相邻的 ## 编码精度 -S2 中的 Hilbert 曲线编码由30位组成，每一位代表一层划分。下表是各层单个 cell 的面积和 cell 个数。 +S2 中的 Hilbert 曲线编码由 30 位组成，每一位代表一层划分。下表是各层单个 cell 的面积和 cell 个数。 | **level** | **min area** | **max area** | **average area** | **units** | **Number of cells** | |-----------|--------------|--------------|------------------|-----------|---------------------| @@ -87,7 +87,7 @@ GEO 索引数据独立于原始数据，两类数据存储在不同的 Pegasus ### hashkey -hashkey 直接由一维编码的前缀构成。比如在一个用户场景中，范围查询都是集中在 10km 半径内的圆形范围，实际测试结果是将 hashkey 长度定为`14`（1位face，1位分隔符`/`，12位Hilbert编码）能取得更好的性能。 +hashkey 由一维编码的前缀构成。比如在一个用户场景中，将 hashkey 长度定为`14`（1位face，1位分隔符`/`，12位Hilbert编码）能取得更好的性能。 > 那么，最小搜索层就为12 @@ -102,8 +102,8 @@ hashkey 直接由一维编码的前缀构成。比如在一个用户场景中， ### sortkey 为了满足不同半径范围、不同精度的查询，我们把 CellId 剩下的 18 位全部放到 sortkey 中。 -- 在进行较大范围的临近查询时，取更少的 sortkey 位数（对应的 CellId 更短）作为前缀，进行数据 scan 查询，这样可以减少数据 scan 的次数 -- 在进行较小范围的临近查询或点查询时，取更多的 sortkey 位数（对应的 CellId 更长）作为前缀，进行数据 scan 查询，这样可以减少数据 scan 的范围 +- 在进行较大半径的范围的查询时，取更少的 sortkey 位数（对应的 CellId 更短）作为前缀，进行数据 scan 查询，这样可以减少数据 scan 的次数 +- 在进行较小半径的范围的查询或点查询时，取更多的 sortkey 位数（对应的 CellId 更长）作为前缀，进行数据 scan 查询，这样可以减少数据 scan 的范围 这可以在不修改底层存储数据的前提下，让应用层保持比较高的灵活性。 @@ -111,7 +111,7 @@ hashkey 直接由一维编码的前缀构成。比如在一个用户场景中， > > 参考：[S2 coverings](http://s2geometry.io/devguide/examples/coverings) -尽管在第30层时，cell 的面积已经足够小（<1cm^2），但仍有可能两条数据落在同一个 cell 里，所以需要在 CellId 编码的基础上，解决 key 冲突问题。Pegasus 将**原始表**的 hashkey 和 sortkey 联合起来，追加在 GEO 表的 sortkey 之后。 +尽管在第30层时，cell 的面积已经足够小（ < 1cm^2），但仍有可能两条数据落在同一个 cell 里，所以需要在 CellId 编码的基础上，解决 key 冲突问题。Pegasus 将**原始表**的 hashkey 和 sortkey 联合起来，追加在 GEO 索引表的 sortkey 之后。 ``` CellId @@ -123,11 +123,11 @@ hashkey 直接由一维编码的前缀构成。比如在一个用户场景中， ### value -使用 GEO 特性所存储的 value 必须能够解析出经纬度，具体的解析方式参考[自定义extrator](/api/geo#%E8%87%AA%E5%AE%9A%E4%B9%89extrator)。 +使用 Pegasus GEO 特性时，所存储的 value 必须能够解析出经纬度，具体的解析方式参考[Value extractor](/api/geo#value_extractor)。 -GEO 索引表的 value 跟原始表的 value 完全相同。这里会存在一份冗余数据，使用空间换时间的方式避免二次索引。 +GEO 索引表的 value 跟原始表的 value 完全相同，因此会存在一份冗余数据，使用空间换时间的方式避免二次索引。 -> 如果确实有在 GEO 的单条 value 中存储较大数据的需求，又想节省磁盘空间，可以手动实现二次索引，即在 GEO value 中存储二级索引的 key，再在另外的表中存储真实的大 value。 +> 如果确实有在单条 POI 中存储较大数据的需求，又想节省磁盘空间，可以手动实现二次索引，即在 GEO value 中存储二级索引的 key，再在另外的表中存储实际的大 value。 ## 数据更新 @@ -135,11 +135,11 @@ GEO 索引表的 value 跟原始表的 value 完全相同。这里会存在一 `set` 操作会同时更新上述两个表的数据，即 Pegasus 原始表数据和 GEO 索引表数据，数据构造方式也如上所述。 -`set`操作的 hashkey，sortkey 是用户自己的格式，使用 GEO API 时并不做约束。两个表的数据转储对用户是透明的，由 GEO Client 自动完成。 +`set`操作的 hashkey，sortkey 是用户自己的格式，使用 GEO API 时并不做约束。两个表的数据同步对用户是透明的，由 GEO Client 自动完成。 使用 Redis GEO API 时， 参考 [GEO API](/api/redis#geo-api)。 -在 Pegasus 实现中，`set`操作会首先尝试读取出已有的数据，如果数据不存在，则直接向两个表中写入数据。如果数据已存在，会先将老的 GEO 索引数据清理掉后，再写入新数据。因为新老数据的索引数据 可能是不一样的（即新老 value 根据 extractor 解析得到的经纬度不一样），若不清理，GEO 索引表中将存在垃圾数据，造成磁盘空间的浪费，也会在进行地理范围查询时（即`GEORADIUS`）查到脏数据。 +在 Pegasus 实现中，`set`操作会首先尝试读取出已有的数据，如果数据不存在，则直接向两个表中写入数据。如果数据已存在，会先将老的 GEO 索引数据清理掉后，再写入新数据。因为新老数据的索引数据 `` 可能是不一样的（即新老 value 根据 extractor 解析得到的经纬度不一样），若不清理，GEO 索引表中将存在垃圾数据，造成磁盘空间的浪费，也会在进行地理范围查询时（即`GEORADIUS`）查到脏数据。 ### del @@ -147,7 +147,7 @@ GEO 索引表的 value 跟原始表的 value 完全相同。这里会存在一 ## 数据查询 -### 思路 +### 设计 地理范围查询会转换成 Pegasus 的多次 scan 操作，一次 scan 对应为一个 CellId 范围内的所有数据扫描。 要想获得更高的性能，就需要减少 scan 的总次数和单次 scan 的数据量，也就是需要减少总的 CellId 数量和单个 CellId 的面积。 @@ -163,11 +163,11 @@ GEO 索引表的 value 跟原始表的 value 完全相同。这里会存在一 ### 查询流程 -以`search_radial`为例，此 API 的意义是给定点和半径，查询出该圆形区域内的所有数据。 +以`search_radial`为例，此 API 的意义是给定点和半径，查询出该圆形区域内的所有 POI 数据。 > 这里我们只讨论圆形区域的数据查询，其他的比如多边形区域的思想是类似的。 -需利用 S2 提供的查询覆盖指定区域的 CellId 集合的 API： +使用 S2 API 来查询覆盖了给定区域的 CellId 集合： ``` // Returns an S2CellUnion that covers the given region and satisfies the current options. @@ -181,19 +181,19 @@ S2CellUnion GetCovering(const S2Region& region); 1. 根据经纬度、半径，求出 S2Cap 圆形区域`C` 2. 根据圆形区域、指定的`最小搜索层`，通过`GetCovering`，求出在`最小搜索层`上的 CellId 集合 3. 遍历这些 CellId，判断 CellId 区域跟圆形区域`C`的关系 - 1. 全覆盖：取该 CellId 内的所有数据 + 1. 全覆盖：取该 CellId 内的所有 POI 数据 2. 半覆盖：将该 CellId 按`最大搜索层`继续拆分，判断拆分后的 sub_CellId 区域与圆形区域`C`的关系 - 1. 覆盖/相交：取该 sub_CellId 的所有数据 + 1. 覆盖/相交：取该 sub_CellId 的所有 POI 数据 2. 不相交：丢弃 > `最小搜索层`，`最大搜索层`的配置参考后文。 -> `最小搜索层`的 CellId 长度确定 GEO 索引表的 hashkey 长度。 +> `最小搜索层`的 CellId 长度确定 GEO 索引表中数据的 hashkey 长度。 -取一个 CellId 的所有数据时，会根据上文的 key 构造规则，构造一对包含这个 CellId 所有数据的`start_sortkey`，`stop_sortkey`，再使用Pegasus的`scan`接口进行数据搜索。 +取一个 CellId 的所有 POI 数据时，会根据上文的 key 构造规则，构造一对包含这个 CellId 所有数据的`start_sortkey`，`stop_sortkey`，再使用Pegasus的`scan`接口进行数据搜索。 -- 对于`3.1`步取到的`最小搜索层` CellId 的编码，它也就是 GEO 索引表中的 hashkey，调用`scan(CellId, "", "")`查询所有数据 - - 比如，一个 12 层的 cell `1/223320022232`被区域完全覆盖，则调用`scan("1/223320022232", "", "")`查询所有数据 -- 对于`3.2.1`步取到的 sub_CellId 集合，hashkey 是它的前缀，调用`scan(sub_CellId_common_prefix, sub_CellId1, sub_CellId2)`搜索数据 +- 对于`3.1`步取到的`最小搜索层` CellId 的编码，它也就是 GEO 索引表中的 hashkey，调用`scan(CellId, "", "")`查询所有 POI 数据 + - 比如，一个 12 层的 cell `1/223320022232`被区域完全覆盖，则调用`scan("1/223320022232", "", "")`查询所有 POI 数据 +- 对于`3.2.1`步取到的 sub_CellId 集合，hashkey 是它的前缀，调用`scan(sub_CellId_common_prefix, sub_CellId1, sub_CellId2)`搜索 POI 数据 - 其中，sub_CellId_common_prefix 是 sub_CellId 集合的公共前缀，长度是 hashkey 的长度。sub_CellId1 和 sub_CellId2 之间连续的所有 sub_CellId 都在集合中，字符串长度是`最大搜索层`减`最小搜索层`的长度 - 比如，一个12层的 cell `1/223320022232`的子区域`0001`,`0002`,`0003`,`0100`才跟目标区域相交时，则调用`scan("1/223320022232", "0001", "0003")`、`scan("1/223320022232", "0100", "0100")` @@ -204,7 +204,7 @@ S2CellUnion GetCovering(const S2Region& region); ### 灵活性 -由于我们存储了完整的 30 层 CellId，所以在实际使用中，可以按照自己的地理数据密度、网络 IO、磁盘 IO等情况调整 API 的`最大搜索层`。 +由于我们存储了完整的 30 层 CellId，所以在实际使用中，可以根据地理数据密度、网络 IO、磁盘 IO等情况调整`最大搜索层`。 > `最大搜索层`默认为`16`。 @@ -223,7 +223,7 @@ max_level = 16 ### 不变性 -由于`最小搜索层`确定了 hashkey 的长度，数据一旦写入 Pegasus 后，`最小搜索层`便不可修改了，因为数据已按这个 hashkey 长度规则固化下来。 +由于`最小搜索层`确定了 GEO 索引数据的 hashkey 的长度，数据一旦写入 Pegasus 后，`最小搜索层`便不可修改了，因为数据已按这个 hashkey 长度规则固化下来。 若要修改，需要重建数据。 @@ -235,17 +235,17 @@ max_level = 16 min_level = 12 ``` -## Value Extrator +## Value extractor 目前 Pegasus 支持从固定格式的 value 中解析出经纬度。经纬度以字符串形式嵌入在 value 中，以`|`分割。 -比如:`.*|115.886447|41.269031|.*`，经纬在 value 中的索引由配置文件中的`latitude_index`和`longitude_index`确定。 +比如：value 可以是`.*|115.886447|41.269031|.*`，经纬度在 value 中的索引由配置文件中的`latitude_index`和`longitude_index`确定。 ## API & Redis Proxy Pegasus GEO 特性的使用有两种方式，一是直接使用 C++ GEO Client，二是使用 Redis Proxy。 -[C++ GEO client代码](https://github.com/apache/incubator-pegasus/blob/master/src/geo/lib/geo_client.h)中有详细的 API 说明，这里不再赘述。 +[C++ GEO client代码](https://github.com/apache/incubator-pegasus/blob/master/src/geo/lib/geo_client.h)中有详细的 API 说明。 ## 配置文件 @@ -259,16 +259,16 @@ GEO API 添加的配置文件如下: min_level = 12 max_level = 16 -; 用于经纬度的extrator +; 用于经纬度的extractor latitude_index = 5 longitude_index = 4 ``` -## 数据导入 +## 批量数据导入 -有的使用场景是用户已经有普通的 KV 数据，需要根据这份已有的 KV 数据转换成如上述的数据格式，我们可以使用 shell 工具里的[copy_data](/docs/tools/shell/#copy_data)功能来实现。比如： +在一些使用场景中，用户已经有 value 中包含经纬度的原始数据表，需要构建上述的 GEO 索引表，则可以使用 shell 工具里的[copy_data](/docs/tools/shell/#copy_data)功能来实现。比如： -在进行`copy_data`操作之前，目标集群以及两个目标表（例如，原始数据表`temp`，GEO 索引数据表 `temp_geo`）都需要提前创建好。 +在进行`copy_data`操作之前，目标集群以及两个目标表（即，原始数据表`temp`，GEO 索引数据表 `temp_geo`）都需要提前创建好。 ``` copy_data -c target_cluster -a temp -g @@ -286,20 +286,21 @@ arguments = redis_cluster temp temp_geo ### 测试环境 -服务器配置： +#### 服务器配置 -- CPU：E5-2620v3 *2 +- CPU：E5-2620v3 * 2 - 内存：128GB -- 存储：480G SSD *8 -- 网卡：1Gb +- 磁盘：容量 480GB SSD * 8 +- 网卡：带宽 1Gb -集群配置： +#### 集群配置 -- 节点数：5 个 Replica Server 节点（使用 v1.9.2 版本） +- Replica Server 节点数：5 个 +- 版本：v1.9.2 - 测试表的 Partition 数：128 - 单条数据大小：120 字节 -针对接口： +#### 测试接口 ``` void async_search_radial(double lat_degrees, @@ -311,7 +312,7 @@ void async_search_radial(double lat_degrees, geo_search_callback_t &&callback); ``` -传递参数： +**参数** - lat_degrees、lng_degrees：每次都选取北京五环内的随机点 - radius_m：如下表第一列，单位米 - count：-1，表示不限定结果数量 diff --git a/_docs/zh/api/redis.md b/_docs/zh/api/redis.md index 297df3ba..e9b55854 100644 --- a/_docs/zh/api/redis.md +++ b/_docs/zh/api/redis.md @@ -79,7 +79,7 @@ Redis 的原生命令请见[这里](https://redis.io/commands) 。 也就是说，在 Pegasus 的 Redis GEO 数据中，不再有 namespace 的概念，全部数据在同一空间 `""` 下。若要区分 namespace，可以在 Pegasus 中创建新的表来实现。 -`SET` 的 value 格式参考[Value Extrator](/api/geo#value-extrator)。 +`SET` 的 value 格式参考[Value Extrator](/api/geo#value-extractor)。 ### SET