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  • 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。

    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。
  • 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e2size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essize参数是永久生效的。

    欢迎斧正,本博客会不定期更新.......