Elasticsearch在应用中常见错误示例解析

目录

• 一 read_only_allow_delete" : "true"
• 二 illegal_argument_exception
• 三 Result window is too large

一 read_only_allow_delete" : "true"

当我们在向某个索引添加一条数据的时候，可能（极少情况）会碰到下面的报错：

{
  "error": {
    "root_cause": [
      {
        "type": "cluster_block_exception",
        "reason": "blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];"
      }
    ],
    "type": "cluster_block_exception",
    "reason": "blocked by: [FORBIDDEN/12/index read-only / allow delete (api)];"
  },
  "status": 403
}

上述报错是说索引现在的状态是只读模式（read-only），如果查看该索引此时的状态：

GET z1/_settings
# 结果如下
{
  "z1" : {
    "settings" : {
      "index" : {
        "number_of_shards" : "5",
        "blocks" : {
          "read_only_allow_delete" : "true"
        },
        "provided_name" : "z1",
        "creation_date" : "1556204559161",
        "number_of_replicas" : "1",
        "uuid" : "3PEevS9xSm-r3tw54p0o9w",
        "version" : {
          "created" : "6050499"
        }
      }
    }
  }
}

可以看到"read_only_allow_delete" : "true"，说明此时无法插入数据，当然，我们也可以模拟出来这个错误：

PUT z1
{
  "mappings": {
    "doc": {
      "properties": {
        "title": {
          "type":"text"
        }
      }
    }
  },
  "settings": {
    "index.blocks.read_only_allow_delete": true
  }
}
PUT z1/doc/1
{
  "title": "es真难学"
}

现在我们如果执行插入数据，就会报开始的错误。那么怎么解决呢？

• 清理磁盘，使占用率低于85%。
• 手动调整该项，具体参考官网

这里介绍一种，我们将该字段重新设置为：

PUT z1/_settings
{
  "index.blocks.read_only_allow_delete": null
}

现在再查看该索引就正常了，也可以正常的插入数据和查询了。

二 illegal_argument_exception

有时候，在聚合中，我们会发现如下报错：

{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [age] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "z2",
        "node": "NRwiP9PLRFCTJA7w3H9eqA",
        "reason": {
          "type": "illegal_argument_exception",
          "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [age] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
        }
      }
    ],
    "caused_by": {
      "type": "illegal_argument_exception",
      "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [age] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead.",
      "caused_by": {
        "type": "illegal_argument_exception",
        "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [age] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
      }
    }
  },
  "status": 400
}

这是怎么回事呢？是因为，聚合查询时，指定字段不能是text类型。比如下列示例：

PUT z2/doc/1
{
  "age":"18"
}
PUT z2/doc/2
{
  "age":20
}
GET z2/doc/_search
{
  "query": {
    "match_all": {}
  },
  "aggs": {
    "my_sum": {
      "sum": {
        "field": "age"
      }
    }
  }
}

当我们向elasticsearch中，添加一条数据时（此时，如果索引存在则直接新增或者更新文档，不存在则先创建索引），首先检查该age字段的映射类型。

如上示例中，我们添加第一篇文档时（z1索引不存在），elasticsearch会自动的创建索引，然后为age字段创建映射关系（es就猜此时age字段的值是什么类型，如果发现是text类型，那么存储该字段的映射类型就是text），此时age字段的值是text类型，所以，第二条插入数据，age的值也是text类型，而不是我们看到的long类型。我们可以查看一下该索引的mappings信息：

GET z2/_mapping
# mapping信息如下
{
  "z2" : {
    "mappings" : {
      "doc" : {
        "properties" : {
          "age" : {
            "type" : "text",
            "fields" : {
              "keyword" : {
                "type" : "keyword",
                "ignore_above" : 256
              }
            }
          }
        }
      }
    }
  }
}

上述返回结果发现，age类型是text。而该类型又不支持聚合，所以，就会报错了。解决办法就是：

• 如果选择动态创建一篇文档，映射关系取决于你添加的第一条文档的各字段都对应什么类型。而不是我们看到的那样，第一次是text，第二次不加引号，就是long类型了不是这样的。
• 如果嫌弃上面的解决办法麻烦，那就选择手动创建映射关系。首先指定好各字段对应什么类型。后续才不至于出错。

三 Result window is too large

很多时候，我们在查询文档时，一次查询结果很可能会有很多，而elasticsearch一次返回多少条结果，由size参数决定：

GET e2/doc/_search
{
  "size": 100000,
  "query": {
    "match_all": {}
  }
}

而默认是最多范围一万条，那么当我们的请求超过一万条时（比如有十万条），就会报：

Result window is too large, from + size must be less than or equal to: [10000] but was [100000]. See the scroll api for a more efficient way to request large data sets. This limit can be set by changing the [index.max_result_window] index level setting.

意思是一次请求返回的结果太大，可以另行参考 scroll API或者设置index.max_result_window参数手动调整size的最大默认值：

# kibana中设置
PUT e2/_settings
{
  "index": {
    "max_result_window": "100000"
  }
}
# Python中设置
from elasticsearch import Elasticsearch
es = Elasticsearch()
es.indices.put_settings(index='e2', body={"index": {"max_result_window": 100000}})

如上例，我们手动调整索引e2的size参数最大默认值到十万，这时，一次查询结果只要不超过10万就都会一次返回。

注意，这个设置对于索引es的size参数是永久生效的。

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