官网地址
https://impala.apache.org/docs/build/html/topics/impala_appx_count_distinct.html
When the APPX_COUNT_DISTINCT query option is set to TRUE, Impala implicitly converts COUNT(DISTINCT) operations to the NDV() function calls. The resulting count is approximate rather than precise. Enable the query option when a tolerable amount of error is acceptable in order to obtain faster query results than with a COUNT (DISTINCT) queries.
Type: Boolean; recognized values are 1 and 0, or true and false; any other value interpreted as false
Default: false (shown as 0 in output of SET statement)
简单的来说就是设置APPX_COUNT_DISTINCT=1后 也就是true 会在select count(distinct column)的时候查询的更快但是查询结果不一定准确
实战
select count(distinct company_id) from ia_fdw_b_profile_product_info--4.97s 27664540
set APPX_COUNT_DISTINCT=1
select count(distinct company_id) from ia_fdw_b_profile_product_info--1.38s 29165564
这里可以看到设置之后查询速度快了几倍，但是误差也不小，所以慎用。。。
NDV()
这里提到了一个ndv函数，但是我在show functions的时候没看到，后面凑巧在官网看到了。
也学习一番
https://impala.apache.org/docs/build/html/topics/impala_ndv.html#ndv
简介
An aggregate function that returns an approximate value similar to the result of COUNT(DISTINCT col), the "number of distinct values". It is much faster than the combination of COUNT and DISTINCT, and uses a constant amount of memory and thus is less memory-intensive for columns with high cardinality.
一种聚合函数，返回的值与COUNT(DISTINCT col)差不多，对值去重。它比count(distinct )快的多，并使用固定的内存量，因此对于具有高基数的列，内存占用较少。
语法:
NDV([DISTINCT | ALL] expression [,scale])
说实话这个表达式我还没看懂。。 举例使用就是 select ndv(colum) from table =select count(distinct column) from table。这个scale是一个精度，但是根本写了就报错。
Note: The optional argument scale must be an integer and can be in the range from 1 to 10 and maps to a precision used by the HyperLogLog (HLL) algorithm with the following mapping formula:
precision = scale + 8
Therefore a scale of 1 is mapped to a precision of 9 and a scale of 10 is mapped to a precision of 18.
Without the optional argument, the precision which determines the total number of different estimators in the HLL algorithm will be still 10.
A large precision value generally produces a better estimation with less error than a small precision value. This is due to the extra number of estimators involved. The expense is at the need of extra memory. For a given precision p, the amount of memory used by the HLL algorithm is in the order of 2^p bytes.
When provided a scale of 10 against a total of 22 distinct data sets loaded into external Impala tables, the error will be computed as abs(<true_unique_value> - <estimated_unique_value>) / <true_unique_value>
The scale of 10, mapped to the precision of 18, yielded the worst estimation error at 0.42% (for one set of 10 million integers), and average error no more than 0.17%. This was at the cost of 256Kb of memory for the internal data structure per evaluation of the HLL algorithm.
官方案例
Examples:
The following example queries a billion-row table to illustrate the relative performance of COUNT(DISTINCT) and NDV(). It shows how COUNT(DISTINCT) gives a precise answer, but is inefficient for large-scale data where an approximate result is sufficient. The NDV() function gives an approximate result but is much faster.
select count(distinct col1) from sample_data;
+---------------------+
| count(distinct col1)|
+---------------------+
| 100000 |
+---------------------+
Fetched 1 row(s) in 20.13s
select cast(ndv(col1) as bigint) as col1 from sample_data;
+----------+
| col1 |
+----------+
| 139017 |
+----------+
Fetched 1 row(s) in 8.91s
The following example shows how you can code multiple NDV() calls in a single query, to easily learn which columns have substantially more or fewer distinct values. This technique is faster than running a sequence of queries with COUNT(DISTINCT) calls.
select cast(ndv(col1) as bigint) as col1, cast(ndv(col2) as bigint) as col2,
cast(ndv(col3) as bigint) as col3, cast(ndv(col4) as bigint) as col4
from sample_data;
+----------+-----------+------------+-----------+
| col1 | col2 | col3 | col4 |
+----------+-----------+------------+-----------+
| 139017 | 282 | 46 | 145636240 |
+----------+-----------+------------+-----------+
Fetched 1 row(s) in 34.97s
select count(distinct col1) from sample_data;
+---------------------+
| count(distinct col1)|
+---------------------+
| 100000 |
+---------------------+
Fetched 1 row(s) in 20.13s
select count(distinct col2) from sample_data;
+----------------------+
| count(distinct col2) |
+----------------------+
| 278 |
+----------------------+
Fetched 1 row(s) in 20.09s
select count(distinct col3) from sample_data;
+-----------------------+
| count(distinct col3) |
+-----------------------+
| 46 |
+-----------------------+
Fetched 1 row(s) in 19.12s
select count(distinct col4) from sample_data;
+----------------------+
| count(distinct col4) |
+----------------------+
| 147135880 |
+----------------------+
Fetched 1 row(s) in 266.95s
其实我到这里也没看太懂这个函数有什么用？唯一的用处就是算去重值的时候会快一点。。。
