GeoMesa-空间数据存储引擎入门学习手册

朱雀 2023-10-04 12:11 34阅读 0赞

## GeoMesa-空间数据存储引擎 ##

geomesa简介、架构体系、数据存储、spark等

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70]

### **第一部分：GeoMesa**简介 ###

*  GeoMesa是一款开源的基于分布式计算系统的⾯面向海海量量时空数据查询与分析的⼯工具包
 *  GeoMesa基于GeoToolsAPI进⾏行行设计，与GeoServer等进⾏行行集成提供OGC标准的服务。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 1]

*  ⽀支持多种可扩展的、基于云端的数据存储架构，包括ApacheAccumulo,HBase，Cassandra，Google Bigtable，以及⽤用于流计算的Apache Kafka 。

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*  提供了了Spark，并增加了了正对空间数据的UDT、UDF和UDAF，⽅方便便⽤用户直接使⽤用SparkSQL进⾏行行空间数据查询与分析。
 *  Git 地址：https://github.com/locationtech/geomesa。
 *  Build地址：https://github.com/locationtech/geomesa/releases

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 3]

### **第二部分：GeoMesa**架构体系 ###

#### 1：模块划分 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 4]

*  ##### **接口层** #####
    
     *  [6.1. GeoTools Feature Types][]
     *  [6.2. Index Overview][]
     *  [6.3. Index Basics][]
     *  [6.4. Index Versioning][]
     *  [6.5. Index Configuration][]
     *  [6.6. Runtime Configuration][]
     *  [6.7. Query Planning][]
     *  [6.8. Explaining Query Plans][]
     *  [6.9. Query Properties][]
     *  [6.10. Filter Functions][]
     *  [6.11. Analytic Querying][]
     *  [6.12. Authorizations][]
     *  [6.13. Query Auditing][]
 *  [6.14. Moving and Migrating Data][]
    
     *  [6.15. Reserved Words][]
 *  ##### 命令行 #####
    
    [Command-Line Tools][]
 *  ##### 底层存储 #####
    
     *  [HBase Data Store][]
     *  [Accumulo Data Store][]
     *  [Bigtable Data Store][]
     *  [Cassandra Data Store][]
     *  [Kafka Data Store][]
     *  [Redis Data Store][]
     *  [FileSystem Data Store][]
     *  [Kudu Data Store][]
     *  [Lambda Data Store][]

#### 2：时空索引-R树 ####

*  据R树的这种数据结构，当我们需要进行一个高维空间查询时，我们只需要遍历少数几个叶子结点所包含的指针，查看这些指针指向的数据是否满足要求即可。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 5]

*  Oracle Spatial、MySQL Spatial、PostgreSQL(PostGIS) 都是基于R树进行空 间搜索操作，即对空间字段(Geometry Column)创建R树索引。
 *  R树存在的问题:
    
    1.  单独创建索引⽂文件
    2.  数据更更新问题：为了了达到平衡状态，新插⼊入数据需要更更新整个R树 。
    3.  不不适合NoSQL的结构  
        HBase本身只提供基于⾏行行键和全表扫描的查询，⽽而⾏行行键索引单⼀一，对于多维度的查询困难。

#### 3：GeoHash ####

##### 1简介 #####

GeoHash是一种地址编码方法。他能够把二维的空间经纬度数据编码成一个字符串。GeoHash具有以下特点：  
1、GeoHash用一个字符串表示经度和纬度两个坐标。在数据库中可以实现在一列上应用索引  
2、GeoHash表示的并不是一个点，而是一个区域；  
3、GeoHash编码的前缀可以表示更大的区域。例如wx4g0ec1，它的前缀wx4g0e表示包含编码wx4g0ec1在内的更大范围。 这个特性可以用于附近地点搜索 。

##### 2：GeoHash的计算过程： #####

###### 1.将经纬度转换成二进制： ######

比如这样一个点（39.923201, 116.390705） 纬度的范围是（-90，90），其中间值为0。对于纬度39.923201，在区间（0，90）中，因此得到一个1；（0，90）区间的中间值为45度，纬度39.923201小于45，因此得到一个0，依次计算下去，即可得到纬度的二进制表示。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 6]

最后得到纬度的二进制表示为： 10111000110001111001  
同理可以得到经度116.390705的二进制表示为： 11010010110001000100

###### 2.合并纬度、经度的二进制： ######

合并方法是将经度、纬度二进制按照奇偶位合并： 1110011101001000111100000011010101100001

如下图：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 7]

###### 3：按照Base32进行编码： ######

Base32编码表（其中一种）：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 8]

将上述合并后二进制编码后结果为： wx4g0ec1

##### 3：特点： #####

*  字符串越长，表示的范围越小越精确;字符串长度越小，表示的范围越大越宽泛。
 *  字符串越相似表示距离越相近。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 9]

#### 4：GeoMesa时空索引 ####

##### 1：各种空间填充曲线 #####

![fill-line.png][]

##### 2：空间查询 #####

1.  用户定义查询窗口
2.  层次划分
3.  计算查询范围(Range)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 10]

##### 3：GeoMesa时空索引 #####

GeoMesa使用了基于Z-order填充曲线的GeoHash空间索引技术，  
并针对时间维度进行了扩展，具体分为:  
• Z2:空间，点索引  
• Z3:时间+空间，点索引  
• XZ2:空间，线\\面索引  
• XZ3:时间+空间，线\\面索引。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 11]

git实现：https://github.com/locationtech/sfcurve

https://github.com/locationtech/geomesa/tree/master/geomesa-z3

#### 5：GeoMesa HBase 索引 ####

1.  RowKey设计

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*  属性索引  
    ![在这里插入图片描述][20210429143243429.png]
 *  Z-Index Shards: 预拆分，范围为1-127，默认为4。  
    ![在这里插入图片描述][20210429143306687.png]
 *  Z-Index Time Interval  
    ![在这里插入图片描述][2021042914332713.png]

1.  查询：
    
     *  可以按照属性查询
     *  可以按照空间范围查询
     *  可以按照时间查询
     *  Geomesa会综合选择一种最快的查询方式执行

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### 第三部分：GeoMesa 数据存储 ###

#### 1：数据结构 ####

**SimpleFeatureType**：空间数据结构描述，包含空间WKT、时间信息、属性信息等。

import org.locationtech.geomesa.utils.interop.SimpleFeatureTypes;
    SimpleFeatureTypes.createType("example", "name:String,dtg:Date,*geom:Point:srid=4326");

**属性类型**

<table> 
 <thead> 
  <tr> 
   <th>Attribute Type</th> 
   <th>Binding</th> 
   <th>Indexable</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>String</td> 
   <td>java.lang.String</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Integer</td> 
   <td>java.lang.Integer</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Double</td> 
   <td>java.lang.Double</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Long</td> 
   <td>java.lang.Long</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Float</td> 
   <td>java.lang.Float</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Boolean</td> 
   <td>java.lang.Boolean</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>UUID</td> 
   <td>java.util.UUID</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Date</td> 
   <td>java.util.Date</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Timestamp</td> 
   <td>java.sql.Timestamp</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Point</td> 
   <td>org.locationtech.jts.geom.Point</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>LineString</td> 
   <td>org.locationtech.jts.geom.LineString</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Polygon</td> 
   <td>org.locationtech.jts.geom.Polygon</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>MultiPoint</td> 
   <td>org.locationtech.jts.geom.MultiPoint</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>MultiLineString</td> 
   <td>org.locationtech.jts.geom.MultiLineString</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>MultiPolygon</td> 
   <td>org.locationtech.jts.geom.MultiPolygon</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>GeometryCollection</td> 
   <td>org.locationtech.jts.geom.GeometryCollection</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Geometry</td> 
   <td>org.locationtech.jts.geom.Geometry</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>List[A]</td> 
   <td>java.util.List</td> 
   <td>Yes</td> 
  </tr> 
  <tr> 
   <td>Map[A,B]</td> 
   <td>java.util.Map&lt;A, B&gt;</td> 
   <td>No</td> 
  </tr> 
  <tr> 
   <td>Bytes</td> 
   <td>byte[]</td> 
   <td>No</td> 
  </tr> 
 </tbody> 
</table>

### 第四部分：GeoMesa Hbase应用 ###

#### 1：Geomesa Hbase操作 ####

#### 2：GeoServer集合 Geomesa ####

Geomesa实现了GeoTools接口，提供了基于HTTP，方法和标准OGC服务的访问形式。

*  [Web Feature Service (WFS)][Web Feature Service _WFS]
 *  [Web Mapping Service (WMS)][Web Mapping Service _WMS]
 *  [Web Processing Service (WPS)][Web Processing Service _WPS]
 *  [Web Coverage Service (WCS)][Web Coverage Service _WCS]
    
    ###### 数据更新流程 ######

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###### 数据查询流程 ######

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### 第五部分：Geomesa Spark ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 16]

#### 1：geomesa-spark-jts ：基于Spark 的JTS控件库。 ####

**依赖包**：

<dependency>
      <groupId>org.locationtech.geomesa</groupId>
      <artifactId>geomesa-spark-jts_2.11</artifactId>
    </dependency>

**从文件系统读取**

import org.locationtech.jts.geom._
    import org.apache.spark.sql.types._
    import org.locationtech.geomesa.spark.jts._
    
    import spark.implicits._
    
    val schema = StructType(Array(
      StructField("name",StringType, nullable=false),
      StructField("pointText", StringType, nullable=false),
      StructField("polygonText", StringType, nullable=false),
      StructField("latitude", DoubleType, nullable=false),
      StructField("longitude", DoubleType, nullable=false)))
    
    val dataFile = this.getClass.getClassLoader.getResource("jts-example.csv").getPath
    val df = spark.read
      .schema(schema)
      .option("sep", "-")
      .option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ")
      .csv(dataFile)
    
    val alteredDF = df
      .withColumn("polygon", st_polygonFromText($"polygonText"))
      .withColumn("point", st_makePoint($"latitude", $"longitude"))

**构造df**

import spark.implicits._
    val point = new GeometryFactory().createPoint(new Coordinate(3.4, 5.6))
    val df = Seq(point).toDF("point")

#### 2：geomesa-spark-core：基于Geotools实现的Spark核心库，将空间数据转换为RDD。 ####

包括SimpleFeatures、 Multiple backends （Accumulo, HBase, FileSystem, Kudu, [GeoMesa Convert][] library转换后文件系统, 其他 GeoTools 数据源）

#### 3：geomesa-spark-sql：基于SparkSQl 的空间查询库 ####

// DataStore params to a hypothetical GeoMesa Accumulo table
    val dsParams = Map(
      "accumulo.instance.id"   -> "instance",
      "accumulo.zookeepers"    -> "zoo1,zoo2,zoo3",
      "accumulo.user"          -> "user",
      "accumulo.password"      -> "*****",
      "accumulo.catalog"       -> "geomesa_catalog",
      "geomesa.security.auths" -> "USER,ADMIN")
    
    // Create SparkSession
    val sparkSession = SparkSession.builder()
      .appName("testSpark")
      .config("spark.sql.crossJoin.enabled", "true")
      .master("local[*]")
      .getOrCreate()
    
    // Create DataFrame using the "geomesa" format
    val dataFrame = sparkSession.read
      .format("geomesa")
      .options(dsParams)
      .option("geomesa.feature", "chicago")
      .load()
    dataFrame.createOrReplaceTempView("chicago")
    
    // Query against the "chicago" schema
    val sqlQuery = "select * from chicago where st_contains(st_makeBBOX(0.0, 0.0, 90.0, 90.0), geom)"
    val resultDataFrame = sparkSession.sql(sqlQuery)
    
    resultDataFrame.show
    /*
    +-------+------+-----------+--------------------+-----------------+
    |__fid__|arrest|case_number|                 dtg|             geom|
    +-------+------+-----------+--------------------+-----------------+
    |      4|  true|          4|2016-01-04 00:00:...|POINT (76.5 38.5)|
    |      5|  true|          5|2016-01-05 00:00:...|    POINT (77 38)|
    |      6|  true|          6|2016-01-06 00:00:...|    POINT (78 39)|
    |      7|  true|          7|2016-01-07 00:00:...|    POINT (20 20)|
    |      9|  true|          9|2016-01-09 00:00:...|    POINT (50 50)|
    +-------+------+-----------+--------------------+-----------------+
    */

### 第六部分：Cassandra应用 ###

1：Cassandra config

spring.data.cassandra.cluster-name=Test Cluster  
spring.data.cassandra.keyspace-name= user\_space  
spring.data.cassandra.contact-points=127.0.0.1  
spring.data.cassandra.port=9042

2：run test

java -cp geomesa-tutorials-cassandra/geomesa-tutorials-cassandra-quickstart/target/geomesa-tutorials-cassandra-quickstart-2.4.0-SNAPSHOT.jar org.geomesa.example.cassandra.CassandraQuickStart --cassandra.contact.point 127.0.0.1:9042 --cassandra.keyspace geomesa --cassandra.catalog sample\_table

3：output

bin/geomesa-cassandra export --output-format leaflet --contact-point 127.0.0.1:9042 --key-space geomesa --catalog sample\_table

### 第七部分：其他应用 ###

#### 1：GeoMesa Kafka DataStore ####

•使⽤用Kafka作为数据存储DataStore  
•通过GeoTools DataStore标准接⼝口进⾏行行访问

•Consumer与Producer可以分布在不不同server

•⽀支持要素缓存，定时写⼊入kafka

\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-ENViAIoI-1619677430713)(resource/kafka.png)\]

#### 2：GeoMesa Lambda DataStore ####

*  数据存储在两个层:transienttier(Kafka)和 a persistent tier(HBase)
 *  数据定时写⼊持久层
 *  使⽤用ZK同步数据缓存状态，保证数据一次写⼊
 *  进⾏行行数据查询会从两个存储层分别进⾏， 然后合并查询结果返回给⽤用户。

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk_size_16_color_FFFFFF_t_70 17]

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[6.1. GeoTools Feature Types]: https://www.geomesa.org/documentation/user/datastores/attributes.html
[6.2. Index Overview]: https://www.geomesa.org/documentation/user/datastores/index_overview.html
[6.3. Index Basics]: https://www.geomesa.org/documentation/user/datastores/index_basics.html
[6.4. Index Versioning]: https://www.geomesa.org/documentation/user/datastores/index_basics.html#index-versioning
[6.5. Index Configuration]: https://www.geomesa.org/documentation/user/datastores/index_config.html
[6.6. Runtime Configuration]: https://www.geomesa.org/documentation/user/datastores/runtime_config.html
[6.7. Query Planning]: https://www.geomesa.org/documentation/user/datastores/query_planning.html
[6.8. Explaining Query Plans]: https://www.geomesa.org/documentation/user/datastores/query_planning.html#explaining-query-plans
[6.9. Query Properties]: https://www.geomesa.org/documentation/user/datastores/query_config.html
[6.10. Filter Functions]: https://www.geomesa.org/documentation/user/datastores/filter_functions.html
[6.11. Analytic Querying]: https://www.geomesa.org/documentation/user/datastores/analytic_queries.html
[6.12. Authorizations]: https://www.geomesa.org/documentation/user/datastores/authorizations.html
[6.13. Query Auditing]: https://www.geomesa.org/documentation/user/datastores/auditing.html
[6.14. Moving and Migrating Data]: https://www.geomesa.org/documentation/user/datastores/data.html
[6.15. Reserved Words]: https://www.geomesa.org/documentation/user/datastores/reserved_words.html
[Command-Line Tools]: https://www.geomesa.org/documentation/user/cli/index.html
[HBase Data Store]: https://www.geomesa.org/documentation/user/hbase/index.html
[Accumulo Data Store]: https://www.geomesa.org/documentation/user/accumulo/index.html
[Bigtable Data Store]: https://www.geomesa.org/documentation/user/bigtable/index.html
[Cassandra Data Store]: https://www.geomesa.org/documentation/user/cassandra/index.html
[Kafka Data Store]: https://www.geomesa.org/documentation/user/kafka/index.html
[Redis Data Store]: https://www.geomesa.org/documentation/user/redis/index.html
[FileSystem Data Store]: https://www.geomesa.org/documentation/user/filesystem/index.html
[Kudu Data Store]: https://www.geomesa.org/documentation/user/kudu/index.html
[Lambda Data Store]: https://www.geomesa.org/documentation/user/lambda/index.html
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[fill-line.png]: https://img-blog.csdnimg.cn/20210429143054818.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2E3NzIzMDQ0MTk=,size_16,color_FFFFFF,t_70
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[20210429143243429.png]: https://img-blog.csdnimg.cn/20210429143243429.png
[20210429143306687.png]: https://img-blog.csdnimg.cn/20210429143306687.png
[2021042914332713.png]: https://img-blog.csdnimg.cn/2021042914332713.png
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[Web Feature Service _WFS]: http://www.opengeospatial.org/standards/wfs
[Web Mapping Service _WMS]: http://www.opengeospatial.org/standards/wms
[Web Processing Service _WPS]: http://www.opengeospatial.org/standards/wps
[Web Coverage Service _WCS]: http://www.opengeospatial.org/standards/wcs
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[GeoMesa Convert]: https://www.geomesa.org/documentation/user/convert/index.html#converters
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