【12】把 Elasticsearch 当数据库使：聚合后再计算

2024年4月21日 179次阅读 来源: taowen

使用 https://github.com/taowen/es-… 可以用 SQL 进行 elasticsearch 的查询。前面我们使用sum或者avg的输入都是document的字段。如果需要对聚合的结果再次进行sum或者avg也是支持的。

特别注意，这里的语法和常规的SQL聚合的语法是反着来的。SQL的语义是从行，到组，再到更大的组。而Elasticsearch的bucket的概念，是从整体，到细分，再到更细分。所以es-monitor的表达方式是按照elasticsearch的习惯来搞的，bucket不断被子sql细分。然后再被select的projection给聚合回来。

MAX(sum_this_year)

SQL

$ cat << EOF | ./es_query.py http://127.0.0.1:9200
    WITH all_symbols AS (SELECT MAX(sum_this_year) AS max_all_times FROM symbol);
    SELECT ipo_year, SUM(market_cap) AS sum_this_year FROM all_symbols 
        GROUP BY ipo_year LIMIT 5
EOF
{"_bucket_path": ["level2"], "max_all_times": 353502983870.0, "ipo_year": 2014, "sum_this_year": 212327524694.0}
{"_bucket_path": ["level2"], "max_all_times": 353502983870.0, "ipo_year": 2015, "sum_this_year": 87950852154.0}
{"_bucket_path": ["level2"], "max_all_times": 353502983870.0, "ipo_year": 2013, "sum_this_year": 138713332228.0}
{"_bucket_path": ["level2"], "max_all_times": 353502983870.0, "ipo_year": 2012, "sum_this_year": 353502983870.0}
{"_bucket_path": ["level2"], "max_all_times": 353502983870.0, "ipo_year": 2011, "sum_this_year": 84547012061.0}

Elasticsearch

{
  "aggs": {
    "max_all_times": {
      "max_bucket": {
        "buckets_path": "ipo_year.sum_this_year"
      }
    }, 
    "ipo_year": {
      "terms": {
        "field": "ipo_year", 
        "size": 5
      }, 
      "aggs": {
        "sum_this_year": {
          "sum": {
            "field": "market_cap"
          }
        }
      }
    }
  }, 
  "size": 0
}

注意这里 max 翻译出来的计算方式是 max_bucket 而不是 max

{
  "hits": {
    "hits": [], 
    "total": 6714, 
    "max_score": 0.0
  }, 
  "_shards": {
    "successful": 1, 
    "failed": 0, 
    "total": 1
  }, 
  "took": 9, 
  "aggregations": {
    "max_all_times": {
      "keys": [
        "2012"
      ], 
      "value": 353502983870.0
    }, 
    "ipo_year": {
      "buckets": [
        {
          "sum_this_year": {
            "value": 212327524694.0
          }, 
          "key": 2014, 
          "doc_count": 390
        }, 
        {
          "sum_this_year": {
            "value": 87950852154.0
          }, 
          "key": 2015, 
          "doc_count": 334
        }, 
        {
          "sum_this_year": {
            "value": 138713332228.0
          }, 
          "key": 2013, 
          "doc_count": 253
        }, 
        {
          "sum_this_year": {
            "value": 353502983870.0
          }, 
          "key": 2012, 
          "doc_count": 147
        }, 
        {
          "sum_this_year": {
            "value": 84547012061.0
          }, 
          "key": 2011, 
          "doc_count": 144
        }
      ], 
      "sum_other_doc_count": 1630, 
      "doc_count_error_upper_bound": 0
    }
  }, 
  "timed_out": false
}

CSUM(max_adj_close)

累加求和，这个只能在histogram聚合后面使用
SQL

$ cat << EOF | ./es_query.py http://127.0.0.1:9200
    SELECT year, MAX(adj_close) AS max_adj_close, CSUM(max_adj_close) FROM quote
    WHERE symbol='AAPL' AND "date" < TIMESTAMP '1985-01-01 00:00:00' 
        GROUP BY date_trunc('year', "date") AS year LIMIT 
EOF
{"max_adj_close": 50.0, "CSUM(max_adj_close)": 50.0, "year": "1981-01-01T00:00:00.000+08:00"}
{"max_adj_close": 51.0, "CSUM(max_adj_close)": 101.0, "year": "1982-01-01T00:00:00.000+08:00"}
{"max_adj_close": 94.0, "CSUM(max_adj_close)": 195.0, "year": "1983-01-01T00:00:00.000+08:00"}
{"max_adj_close": 50.0, "CSUM(max_adj_close)": 245.0, "year": "1984-01-01T00:00:00.000+08:00"}

Elasticsearch

{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "symbol": "AAPL"
          }
        }, 
        {
          "range": {
            "date": {
              "lt": 473356800000
            }
          }
        }
      ]
    }
  }, 
  "aggs": {
    "year": {
      "date_histogram": {
        "field": "date", 
        "interval": "year", 
        "time_zone": "+08:00"
      }, 
      "aggs": {
        "max_adj_close": {
          "max": {
            "field": "adj_close"
          }
        }, 
        "CSUM(max_adj_close)": {
          "cumulative_sum": {
            "buckets_path": "max_adj_close"
          }
        }
      }
    }
  }, 
  "size": 0
}
{
  "hits": {
    "hits": [], 
    "total": 943, 
    "max_score": 0.0
  }, 
  "_shards": {
    "successful": 1, 
    "failed": 0, 
    "total": 1
  }, 
  "took": 32, 
  "aggregations": {
    "year": {
      "buckets": [
        {
          "max_adj_close": {
            "value": 50.0
          }, 
          "CSUM(max_adj_close)": {
            "value": 50.0
          }, 
          "key_as_string": "1981-01-01T00:00:00.000+08:00", 
          "key": 347126400000, 
          "doc_count": 185
        }, 
        {
          "max_adj_close": {
            "value": 51.0
          }, 
          "CSUM(max_adj_close)": {
            "value": 101.0
          }, 
          "key_as_string": "1982-01-01T00:00:00.000+08:00", 
          "key": 378662400000, 
          "doc_count": 253
        }, 
        {
          "max_adj_close": {
            "value": 94.0
          }, 
          "CSUM(max_adj_close)": {
            "value": 195.0
          }, 
          "key_as_string": "1983-01-01T00:00:00.000+08:00", 
          "key": 410198400000, 
          "doc_count": 252
        }, 
        {
          "max_adj_close": {
            "value": 50.0
          }, 
          "CSUM(max_adj_close)": {
            "value": 245.0
          }, 
          "key_as_string": "1984-01-01T00:00:00.000+08:00", 
          "key": 441734400000, 
          "doc_count": 253
        }
      ]
    }
  }, 
  "timed_out": false
}

DERIVATIVE(max_adj_close)

SQL

$ cat << EOF | ./es_query.py http://127.0.0.1:9200
    SELECT year, MAX(adj_close) AS max_adj_close, DERIVATIVE(max_adj_close) FROM quote
    WHERE symbol='AAPL' AND "date" < TIMESTAMP '1985-01-01 00:00:00' 
    GROUP BY date_trunc('year', "date") AS year 
EOF
{"max_adj_close": 50.0, "DERIVATIVE(max_adj_close)": null, "year": "1981-01-01T00:00:00.000+08:00"}
{"max_adj_close": 51.0, "DERIVATIVE(max_adj_close)": 1.0, "year": "1982-01-01T00:00:00.000+08:00"}
{"max_adj_close": 94.0, "DERIVATIVE(max_adj_close)": 43.0, "year": "1983-01-01T00:00:00.000+08:00"}
{"max_adj_close": 50.0, "DERIVATIVE(max_adj_close)": -44.0, "year": "1984-01-01T00:00:00.000+08:00"}

Elasticsearch

{
  "query": {
    "bool": {
      "filter": [
        {
          "term": {
            "symbol": "AAPL"
          }
        }, 
        {
          "range": {
            "date": {
              "lt": 473356800000
            }
          }
        }
      ]
    }
  }, 
  "aggs": {
    "year": {
      "date_histogram": {
        "field": "date", 
        "interval": "year", 
        "time_zone": "+08:00"
      }, 
      "aggs": {
        "max_adj_close": {
          "max": {
            "field": "adj_close"
          }
        }, 
        "DERIVATIVE(max_adj_close)": {
          "derivative": {
            "buckets_path": "max_adj_close"
          }
        }
      }
    }
  }, 
  "size": 0
}
{
  "hits": {
    "hits": [], 
    "total": 943, 
    "max_score": 0.0
  }, 
  "_shards": {
    "successful": 1, 
    "failed": 0, 
    "total": 1
  }, 
  "took": 4, 
  "aggregations": {
    "year": {
      "buckets": [
        {
          "max_adj_close": {
            "value": 50.0
          }, 
          "key_as_string": "1981-01-01T00:00:00.000+08:00", 
          "key": 347126400000, 
          "doc_count": 185
        }, 
        {
          "max_adj_close": {
            "value": 51.0
          }, 
          "DERIVATIVE(max_adj_close)": {
            "value": 1.0
          }, 
          "key_as_string": "1982-01-01T00:00:00.000+08:00", 
          "key": 378662400000, 
          "doc_count": 253
        }, 
        {
          "max_adj_close": {
            "value": 94.0
          }, 
          "DERIVATIVE(max_adj_close)": {
            "value": 43.0
          }, 
          "key_as_string": "1983-01-01T00:00:00.000+08:00", 
          "key": 410198400000, 
          "doc_count": 252
        }, 
        {
          "max_adj_close": {
            "value": 50.0
          }, 
          "DERIVATIVE(max_adj_close)": {
            "value": -44.0
          }, 
          "key_as_string": "1984-01-01T00:00:00.000+08:00", 
          "key": 441734400000, 
          "doc_count": 253
        }
      ]
    }
  }, 
  "timed_out": false
}
    原文作者：taowen
    原文地址: https://segmentfault.com/a/1190000004466154
    本文转自网络文章，转载此文章仅为分享知识，如有侵权，请联系博主进行删除。
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