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\t%s\t%s", bucketKey, "pre_total_fee_sum", subAggMap.get("pre_total_fee_sum")));
System.out.println(String.format("%s\t%s\t%s", bucketKey, "pre_total_fee_avg", subAggMap.get("pre_total_fee_avg")));
System.out.println(String.format("%s\t%s\t%s", bucketKey, "pre_total_fee_percent", subAggMap.get("pre_total_fee_percent")));
System.out.println(String.format("%s\t%s\t%s", bucketKey, "pre_total_fee_percentRank", subAggMap.get("pre_total_fee_percentRank")));
System.out.println(String.format("%s\t%s\t%s", bucketKey, "pre_total_fee_stats", subAggMap.get("pre_total_fee_stats")));
System.out.println(String.format("%s\t%s\t%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。
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