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[6.8] [DOCS] Remove approximate document counts example from term agg docs (#55442) #56043

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[6.8] [DOCS] Remove approximate document counts example from term agg docs (#55442) #56043

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160 changes: 13 additions & 147 deletions docs/reference/aggregations/bucket/terms-aggregation.asciidoc
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Original file line number Diff line number Diff line change
Expand Up @@ -68,7 +68,8 @@ GET /_search
--------------------------------------------------
// CONSOLE
// TEST[s/_search/_search\?filter_path=aggregations/]
<1> `terms` aggregation should be a field of type `keyword` or any other data type suitable for bucket aggregations. In order to use it with `text` you will need to enable

<1> `terms` aggregation should be a field of type `keyword` or any other data type suitable for bucket aggregations. In order to use it with `text` you will need to enable
<<fielddata, fielddata>>.

Response:
Expand Down Expand Up @@ -125,85 +126,10 @@ NOTE: If you want to retrieve **all** terms or all combinations of terms in a ne
[[search-aggregations-bucket-terms-aggregation-approximate-counts]]
==== Document counts are approximate

As described above, the document counts (and the results of any sub aggregations) in the terms aggregation are not always
accurate. This is because each shard provides its own view of what the ordered list of terms should be and these are
combined to give a final view. Consider the following scenario:

A request is made to obtain the top 5 terms in the field product, ordered by descending document count from an index with
3 shards. In this case each shard is asked to give its top 5 terms.

[source,js]
--------------------------------------------------
GET /_search
{
"aggs" : {
"products" : {
"terms" : {
"field" : "product",
"size" : 5
}
}
}
}
--------------------------------------------------
// CONSOLE
// TEST[s/_search/_search\?filter_path=aggregations/]

The terms for each of the three shards are shown below with their
respective document counts in brackets:

[width="100%",cols="^2,^2,^2,^2",options="header"]
|=========================================================
| | Shard A | Shard B | Shard C

| 1 | Product A (25) | Product A (30) | Product A (45)
| 2 | Product B (18) | Product B (25) | Product C (44)
| 3 | Product C (6) | Product F (17) | Product Z (36)
| 4 | Product D (3) | Product Z (16) | Product G (30)
| 5 | Product E (2) | Product G (15) | Product E (29)
| 6 | Product F (2) | Product H (14) | Product H (28)
| 7 | Product G (2) | Product I (10) | Product Q (2)
| 8 | Product H (2) | Product Q (6) | Product D (1)
| 9 | Product I (1) | Product J (6) |
| 10 | Product J (1) | Product C (4) |

|=========================================================

The shards will return their top 5 terms so the results from the shards will be:

[width="100%",cols="^2,^2,^2,^2",options="header"]
|=========================================================
| | Shard A | Shard B | Shard C

| 1 | Product A (25) | Product A (30) | Product A (45)
| 2 | Product B (18) | Product B (25) | Product C (44)
| 3 | Product C (6) | Product F (17) | Product Z (36)
| 4 | Product D (3) | Product Z (16) | Product G (30)
| 5 | Product E (2) | Product G (15) | Product E (29)

|=========================================================

Taking the top 5 results from each of the shards (as requested) and combining them to make a final top 5 list produces
the following:

[width="40%",cols="^2,^2"]
|=========================================================

| 1 | Product A (100)
| 2 | Product Z (52)
| 3 | Product C (50)
| 4 | Product G (45)
| 5 | Product B (43)

|=========================================================

Because Product A was returned from all shards we know that its document count value is accurate. Product C was only
returned by shards A and C so its document count is shown as 50 but this is not an accurate count. Product C exists on
shard B, but its count of 4 was not high enough to put Product C into the top 5 list for that shard. Product Z was also
returned only by 2 shards but the third shard does not contain the term. There is no way of knowing, at the point of
combining the results to produce the final list of terms, that there is an error in the document count for Product C and
not for Product Z. Product H has a document count of 44 across all 3 shards but was not included in the final list of
terms because it did not make it into the top five terms on any of the shards.
Document counts (and the results of any sub aggregations) in the terms
aggregation are not always accurate. Each shard provides its own view of what
the ordered list of terms should be. These views are combined to give a final
view.

==== Shard Size

Expand All @@ -228,35 +154,7 @@ The default `shard_size` is `(size * 1.5 + 10)`.

There are two error values which can be shown on the terms aggregation. The first gives a value for the aggregation as
a whole which represents the maximum potential document count for a term which did not make it into the final list of
terms. This is calculated as the sum of the document count from the last term returned from each shard. For the example
given above the value would be 46 (2 + 15 + 29). This means that in the worst case scenario a term which was not returned
could have the 4th highest document count.

[source,js]
--------------------------------------------------
{
...
"aggregations" : {
"products" : {
"doc_count_error_upper_bound" : 46,
"sum_other_doc_count" : 79,
"buckets" : [
{
"key" : "Product A",
"doc_count" : 100
},
{
"key" : "Product Z",
"doc_count" : 52
}
...
]
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]
// TESTRESPONSE[s/: (\-)?[0-9]+/: $body.$_path/]
terms. This is calculated as the sum of the document count from the last term returned from each shard.

==== Per bucket document count error

Expand All @@ -283,39 +181,7 @@ GET /_search

This shows an error value for each term returned by the aggregation which represents the 'worst case' error in the document count
and can be useful when deciding on a value for the `shard_size` parameter. This is calculated by summing the document counts for
the last term returned by all shards which did not return the term. In the example above the error in the document count for Product C
would be 15 as Shard B was the only shard not to return the term and the document count of the last term it did return was 15.
The actual document count of Product C was 54 so the document count was only actually off by 4 even though the worst case was that
it would be off by 15. Product A, however has an error of 0 for its document count, since every shard returned it we can be confident
that the count returned is accurate.

[source,js]
--------------------------------------------------
{
...
"aggregations" : {
"products" : {
"doc_count_error_upper_bound" : 46,
"sum_other_doc_count" : 79,
"buckets" : [
{
"key" : "Product A",
"doc_count" : 100,
"doc_count_error_upper_bound" : 0
},
{
"key" : "Product Z",
"doc_count" : 52,
"doc_count_error_upper_bound" : 2
}
...
]
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\.//]
// TESTRESPONSE[s/: (\-)?[0-9]+/: $body.$_path/]
the last term returned by all shards which did not return the term.

These errors can only be calculated in this way when the terms are ordered by descending document count. When the aggregation is
ordered by the terms values themselves (either ascending or descending) there is no error in the document count since if a shard
Expand Down Expand Up @@ -689,7 +555,7 @@ GET /_search

===== Filtering Values with partitions

Sometimes there are too many unique terms to process in a single request/response pair so
Sometimes there are too many unique terms to process in a single request/response pair so
it can be useful to break the analysis up into multiple requests.
This can be achieved by grouping the field's values into a number of partitions at query-time and processing
only one partition in each request.
Expand Down Expand Up @@ -729,10 +595,10 @@ GET /_search
This request is finding the last logged access date for a subset of customer accounts because we
might want to expire some customer accounts who haven't been seen for a long while.
The `num_partitions` setting has requested that the unique account_ids are organized evenly into twenty
partitions (0 to 19). and the `partition` setting in this request filters to only consider account_ids falling
partitions (0 to 19). and the `partition` setting in this request filters to only consider account_ids falling
into partition 0. Subsequent requests should ask for partitions 1 then 2 etc to complete the expired-account analysis.

Note that the `size` setting for the number of results returned needs to be tuned with the `num_partitions`.
Note that the `size` setting for the number of results returned needs to be tuned with the `num_partitions`.
For this particular account-expiration example the process for balancing values for `size` and `num_partitions` would be as follows:

1. Use the `cardinality` aggregation to estimate the total number of unique account_id values
Expand All @@ -741,8 +607,8 @@ For this particular account-expiration example the process for balancing values
4. Run a test request

If we have a circuit-breaker error we are trying to do too much in one request and must increase `num_partitions`.
If the request was successful but the last account ID in the date-sorted test response was still an account we might want to
expire then we may be missing accounts of interest and have set our numbers too low. We must either
If the request was successful but the last account ID in the date-sorted test response was still an account we might want to
expire then we may be missing accounts of interest and have set our numbers too low. We must either

* increase the `size` parameter to return more results per partition (could be heavy on memory) or
* increase the `num_partitions` to consider less accounts per request (could increase overall processing time as we need to make more requests)
Expand Down