本文给出如何使用Elasticsearch的Java API做类似SQL的group by聚合。
为了简单起见,只给出一级groupby即group by field1(而不涉及到多级,例如group by field1, field2, ...);如果你需要多级的groupby,在实现上可能需要拆分的更加细致。
即将给出的方法,适用于如下的场景:
场景1:找出分组中的所有桶,例如,select group_name from index_name group by group_name;
场景2:灵活添加一个或者多个聚合函数,例如,select group_name, max(count), avg(count) group by group_name;
1、用法
GroupBy类是我们的实现。
1)测试用例
public static void main(String[] args) { /* * 初始化es客户端 * */ ESClient esClient = new ESClient( "dqa-cluster", "10.93.21.21:9300,10.93.18.34:9300,10.93.18.35:9300,100.90.62.33:9300,100.90.61.14:9300", false); /* * 为了演示, 构造了一个距离查询, 相当于where子句. * */ GeoDistanceRangeQueryBuilder queryBuilder = QueryBuilders.geoDistanceRangeQuery("location") .point(39.971424, 116.398251) .from("0m") .to(String.format("%fm", 500.0)) .includeLower(true) .includeUpper(true) .optimizeBbox("memory") .geoDistance(GeoDistance.SLOPPY_ARC); SearchRequestBuilder search = esClient.getClient().prepareSearch("moon").setTypes("bj") .setSearchType(SearchType.DFS_QUERY_AND_FETCH) .setQuery(queryBuilder); /* * GroupBy类就是我们的实现, 初始化的时候传入的参数依次是, search, 桶命名, 分桶字段, 排序asc * select date as date_group from index group by date; * */ GroupBy groupBy = new GroupBy(search, "date_group", "date", true); /* * 添加各种分组函数 * 这里我实现了10种, 下面是其中的6种 * */ groupBy.addSumAgg("pre_total_fee_sum", "pre_total_fee"); groupBy.addAvgAgg("pre_total_fee_avg", "pre_total_fee"); groupBy.addPercentilesAgg("pre_total_fee_percent", "pre_total_fee"); groupBy.addPercentileRanksAgg("pre_total_fee_percentRank", "pre_total_fee", new double[]{13, 16, 20}); groupBy.addStatsAgg("pre_total_fee_stats", "pre_total_fee"); groupBy.addCardinalityAgg("type_card", "type"); /* * 获取groupBy聚合的结果 * 结果是两级Map, 这里的实现是TreeMap因为要保护桶的排序 * */ Map<String, Object> groupbyResponse = groupBy.getGroupbyResponse(); for (Map.Entry<String, Object> entry : groupbyResponse.entrySet()) { String bucketKey = entry.getKey(); Map<String, String> subAggMap = (Map<String, String>) entry.getValue(); System.out.println(String.format("%s %s %s", bucketKey, "pre_total_fee_sum", subAggMap.get("pre_total_fee_sum"))); System.out.println(String.format("%s %s %s", bucketKey, "pre_total_fee_avg", subAggMap.get("pre_total_fee_avg"))); System.out.println(String.format("%s %s %s", bucketKey, "pre_total_fee_percent", subAggMap.get("pre_total_fee_percent"))); System.out.println(String.format("%s %s %s", bucketKey, "pre_total_fee_percentRank", subAggMap.get("pre_total_fee_percentRank"))); System.out.println(String.format("%s %s %s", bucketKey, "pre_total_fee_stats", subAggMap.get("pre_total_fee_stats"))); System.out.println(String.format("%s %s %s", bucketKey, "type_card", subAggMap.get("type_card"))); } }
2)初始化
初始化的时候,相当于构造了这样一个SQL:select date as date_group from index group by date;
传入search对象,相当于where子句
传入分桶命名, 相当于 as date_group
传入分桶字段,相当于date
传入排序,asc=true
3)初始化完成后,可以添加各种聚合函数,也就是场景2。
GroupBy类里实现了10种聚合函数
4)读取结果
结果的返回是两级Map,为了保护分桶的排序,实现中使用了TreeMap。
这里需要注意的是,有些聚合函数的返回,并不是一个值,而是一组值,如Percentiles、Stats等等,这里我们把这一组值压缩成JSONString了。
5)打印输出
我们以日期进行了分桶,同一个分桶中的聚合结果,sum、avg、cardinality都是单个的值。而percentiles、percentileRanks、stats是压缩的jsonstring。
2、实现
先上代码,然后在后面进行讲解。
public class GroupBy { private SearchRequestBuilder search; private String termsName; private TermsBuilder termsBuilder; private List<Map<String, Object>> subAggList = new ArrayList<Map<String, Object>>(); public GroupBy(SearchRequestBuilder search, String termsName, String fieldName, boolean asc) { this.search = search; this.termsName = termsName; termsBuilder = AggregationBuilders.terms(termsName).field(fieldName).order(Terms.Order.term(asc)).size(0); } private void addSubAggList(String aggName, MetricsAggregationBuilder aggBuilder) { Map<String, Object> subAgg = new HashMap<String, Object>(); subAgg.put("aggName", aggName); subAgg.put("aggBuilder", aggBuilder); subAggList.add(subAgg); } public void addSumAgg(String aggName, String fieldName) { SumBuilder builder = AggregationBuilders.sum(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketSumAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof SumBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public void addCountAgg(String aggName, String fieldName) { ValueCountBuilder builder = AggregationBuilders.count(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketCountAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof ValueCountBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public void addAvgAgg(String aggName, String fieldName) { AvgBuilder builder = AggregationBuilders.avg(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketAvgAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof AvgBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public void addMinAgg(String aggName, String fieldName) { MinBuilder builder = AggregationBuilders.min(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketMinAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof MinBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public void addMaxAgg(String aggName, String fieldName) { MaxBuilder builder = AggregationBuilders.max(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketMaxAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof MaxBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public void addStatsAgg(String aggName, String fieldName) { StatsBuilder builder = AggregationBuilders.stats(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketStatsAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof StatsBuilder) { Stats stats = bucket.getAggregations().get(aggName); JSONObject jsonObject = new JSONObject(); jsonObject.put("min", stats.getMin()); jsonObject.put("max", stats.getMax()); jsonObject.put("sum", stats.getMax()); jsonObject.put("count", stats.getCount()); jsonObject.put("avg", stats.getAvg()); tmpMap.put(aggName, jsonObject.toJSONString()); return true; } else { return false; } } public void addExtendedStatsAgg(String aggName, String fieldName) { ExtendedStatsBuilder builder = AggregationBuilders.extendedStats(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketExtendedStatsAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof ExtendedStatsBuilder) { ExtendedStats extendedStats = bucket.getAggregations().get(aggName); JSONObject jsonObject = new JSONObject(); jsonObject.put("min", extendedStats.getMin()); jsonObject.put("max", extendedStats.getMax()); jsonObject.put("sum", extendedStats.getMax()); jsonObject.put("count", extendedStats.getCount()); jsonObject.put("avg", extendedStats.getAvg()); jsonObject.put("stdDeviation", extendedStats.getStdDeviation()); jsonObject.put("sumOfSquares", extendedStats.getSumOfSquares()); jsonObject.put("variance", extendedStats.getVariance()); tmpMap.put(aggName, jsonObject.toJSONString()); return true; } else { return false; } } public void addPercentilesAgg(String aggName, String fieldName) { PercentilesBuilder builder = AggregationBuilders.percentiles(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public void addPercentilesAgg(String aggName, String fieldName, double[] percentiles) { PercentilesBuilder builder = AggregationBuilders.percentiles(aggName).field(fieldName).percentiles(percentiles); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketPercentilesAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof PercentilesBuilder) { Percentiles percentiles = bucket.getAggregations().get(aggName); JSONObject jsonObject = new JSONObject(); for (Percentile percentile : percentiles) { jsonObject.put(String.valueOf(percentile.getPercent()), percentile.getValue()); } tmpMap.put(aggName, jsonObject.toJSONString()); return true; } else { return false; } } public void addPercentileRanksAgg(String aggName, String fieldName, double[] percentiles) { PercentileRanksBuilder builder = AggregationBuilders.percentileRanks(aggName).field(fieldName).percentiles(percentiles); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketPercentileRanksAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof PercentileRanksBuilder) { PercentileRanks percentileRanks = bucket.getAggregations().get(aggName); JSONObject jsonObject = new JSONObject(); for (Percentile percentile : percentileRanks) { jsonObject.put(String.valueOf(percentile.getPercent()), percentile.getValue()); } tmpMap.put(aggName, jsonObject.toJSONString()); return true; } else { return false; } } public void addCardinalityAgg(String aggName, String fieldName) { CardinalityBuilder builder = AggregationBuilders.cardinality(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); } public boolean bucketCardinalityAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof CardinalityBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } } public List<Terms.Bucket> getTermsBucket() { search.addAggregation(termsBuilder); Terms termsGroup = search.get().getAggregations().get(termsName); return termsGroup.getBuckets(); } public Map<String, Object> getGroupbyResponse() { Map<String, Object> aggResponseMap = new TreeMap<String, Object>(); for (Terms.Bucket bucket : getTermsBucket()) { String bucketKeyAsString = bucket.getKeyAsString(); Map<String, String> tmpMap = new TreeMap<String, String>(); for (Map<String, Object> subAgg : subAggList) { String subAggName = subAgg.get("aggName").toString(); MetricsAggregationBuilder subAggBuilder = (MetricsAggregationBuilder) subAgg.get("aggBuilder"); if (bucketAvgAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketMaxAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketMinAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketSumAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketCountAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketCardinalityAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketPercentileRanksAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketPercentilesAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketExtendedStatsAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketStatsAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; } aggResponseMap.put(bucketKeyAsString, tmpMap); } return aggResponseMap; } }
1)构造函数
构造函数中,核心逻辑是termsBuilder = AggregationBuilders.terms(termsName).field(fieldName).order(Terms.Order.term(asc)).size(0);
实例化了termsBuilder也就是分桶。
后面调用add...函数簇添加聚合函数的时候,都是通过termsBuilder.subAggregation(builder)在分桶的基础上添加了子聚合。
最后在获取结果的时候search.addAggregation(termsBuilder);将termsBuilder添加到查询上,进行聚合查询。
2)添加聚合函数add...函数簇
以sum函数为例
public void addSumAgg(String aggName, String fieldName) { SumBuilder builder = AggregationBuilders.sum(aggName).field(fieldName); termsBuilder.subAggregation(builder); addSubAggList(aggName, builder); }
a)初始化了一个SumBuilder聚合操作,然后作为termsBuilder的子聚合。
b)addSubAggList方法在subAggList属性(subAggList属性是一个List<Map<String, Object>>)上保存了所有添加了的子聚合的名字和builder。这样做是为了在解析结果的时候,知道是哪种type的聚合(instanceof),以便使用不同的逻辑去解析。
private void addSubAggList(String aggName, MetricsAggregationBuilder aggBuilder) { Map<String, Object> subAgg = new HashMap<String, Object>(); subAgg.put("aggName", aggName); subAgg.put("aggBuilder", aggBuilder); subAggList.add(subAgg); }
3)按类型获取结果
还是以sum函数为例
public boolean bucketSumAgg(Terms.Bucket bucket, String aggName, MetricsAggregationBuilder aggBuilder, Map<String, String> tmpMap) { if (aggBuilder instanceof SumBuilder) { tmpMap.put(aggName, bucket.getAggregations().get(aggName).getProperty("value").toString()); return true; } else { return false; } }
a)这里先判断了aggBuilder是哪种类型的(instanceof),如果是SumBuilder类型的,就按照sum的结果类型去读取返回结果。
b)sum的返回结果就是一个值,当遇到percentiles这种类型的,返回结果不是一个值,此时为了简单,我将结果压缩成了jsonstring,也相当于一个值,可以自行参看代码。
c)后面依赖return true实现了一个逻辑,一旦命中了类型,就不再继续判断了,提升效率。
d)tmpMap是外部传入的一个全局接收器,用来存储结果。
4)解析所有的子聚合结果
public Map<String, Object> getGroupbyResponse() { Map<String, Object> aggResponseMap = new TreeMap<String, Object>(); for (Terms.Bucket bucket : getTermsBucket()) { String bucketKeyAsString = bucket.getKeyAsString(); Map<String, String> tmpMap = new TreeMap<String, String>(); for (Map<String, Object> subAgg : subAggList) { String subAggName = subAgg.get("aggName").toString(); MetricsAggregationBuilder subAggBuilder = (MetricsAggregationBuilder) subAgg.get("aggBuilder"); if (bucketAvgAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketMaxAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketMinAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketSumAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketCountAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketCardinalityAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketPercentileRanksAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketPercentilesAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketExtendedStatsAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; if (bucketStatsAgg(bucket, subAggName, subAggBuilder, tmpMap)) continue; } aggResponseMap.put(bucketKeyAsString, tmpMap); } return aggResponseMap; }
这里是解析结果的代码。tmpMap定义为全局接收器。
a)通过遍历subAggList存储的所有子聚合函数,获取所有的子聚合结果,并保存成两级TreeMap。
b)对每个迭代,调用所有的bucket...函数簇,这里通过if判断是否命中类型,如果命中了,就通过continue不再继续检查。
c) aggResponseMap使用treeMap是为了保持bucket的有序。
3、十种聚合函数
最后列出我们实现的十种聚合函数,你可以根据自己的需求继续添加。
1)返回单个值:sum、avg、min、max、count、cardinality(有误差)
2)percentiles:分位数查询,传入分位数,获取分位数上的值;percentileRanks,分位数排名查询,传入值,返回对应的分位数;互为逆向操作。
3)stats和extendedStats,extended聚合更详细的信息max、min、avg、sum、平方和、标准差等。