Doris通过创建外部表方式将Doris的分布式查询规划能力和ES(Elasticsearch)的全文检索能力相结合，提供更完善的OLAP分析场景解决方案，支持：
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ES中的多index分布式Join查询
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Doris和ES中的表联合查询，更复杂的全文检索过滤
创建ES外表后，FE会请求建表指定的主机，获取所有节点的HTTP端口信息以及index的shard分布信息等，如果请求失败会顺序遍历host列表直至成功或完全失败。
执行查询时，会根据FE得到的一些节点信息和index的元数据信息，生成查询计划并发给对应的BE节点，BE节点会根据就近原则即优先请求本地部署的ES节点，BE通过HTTP Scroll方式流式的从ES index的每个分片中并发的获取数据
计算完结果后，返回给client端。
ES节点类型分为主节点、数据节点、协调节点，FE通过主节点获取ES信息，BE直接拉取数据节点获取数据。
实验过程实验环境：doris版本0.14.0，elasticsearch版本7.11.1
doris环境搭建及启动这里就不在叙述了，elasticsearch参考ES环境搭建及后续文章。
一、单节点查询：
1、创建doris外部表
CREATE EXTERNAL TABLE `es_table` ( `id` bigint(20) COMMENT "", `k1` bigint(20) COMMENT "", `k2` datetime COMMENT "", `k3` varchar(20) COMMENT "", `k4` varchar(100) COMMENT "", `k5` float COMMENT "") ENGINE=ELASTICSEARCHPARTITION BY RANGE(`id`)()PROPERTIES ("host" = "http://192.168.244.129:9200","index" = "test”);
2、ES初始化
1、创建test索引
{ "mappings": { "properties": { "k1": { "type": "long", "index": "true" }, "k3": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_max_word" }, "k4": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_max_word" }, "k5": { "type": "float" }, "k2": { "type": "date", "format": "yyyy-MM-dd" } } }}
2、添加数据
{ "k1": 100, "k2": "2020-01-01", "k3": "Trying", "k4": "Trying out Elasticsearch", "k5": 10}
数据添加成功后，在mysql客户端连接doris查询ES数据，看到如下结果代表doris查询ES成功。
3、批量添加数据
POST /_bulk
{"index":{"_index":"test"}}
{ "k1" : 100, "k2": "2020-01-01", "k3": "Trying out Elasticsearch", "k4": "Trying out Elasticsearch", "k5": 10.0}
{"index":{"_index":"test"}}
{ "k1" : 100, "k2": "2020-01-01", "k3": "Trying out Doris", "k4": "Trying out Doris", "k5": 10.0}
{"index":{"_index":"test"}}
{ "k1" : 100, "k2": "2020-01-01", "k3": "Doris On ES", "k4": "Doris On ES", "k5": 10.0}
{"index":{"_index":"test"}}
{ "k1" : 100, "k2": "2020-01-01", "k3": "Doris", "k4": "Doris", "k5": 10.0}
{"index":{"_index":"test"}}
{ "k1" : 100, "k2": "2020-01-01", "k3": "ES", "k4": "ES", "k5": 10.0}
执行模糊匹配查询：
二、JOIN查询：
1、创建外部表
2、ES创建索引test2
{ "mappings": { "properties": { "k1": { "type": "long", "index": "true" }, "k3": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_max_word" }, "k4": { "type": "text", "analyzer": "ik_max_word", "search_analyzer": "ik_max_word" }, "k5": { "type": "float" }, "k2": { "type": "date", "format": "yyyy-MM-dd" } } }}
3、ES添加数据
POST /_bulk{"index":{"_index":"test2"}}{ "k1" : 200, "k2": "2020-02-01", "k3": "Doris e ", "k4": "ES", "k5": 20.0}
4、执行JOIN查询
5、JOIN模糊查询
select * from test ,test2 where test.k1=test2.k1 and esquery (test.k3, '{ "match": { "k3": "ES" } }');
Doris ON ES 今天就介绍到这里了，觉得有用关注：蓝天Java大数据
