This expands the idea implemented in #14074, where Kahan summation was
applied to sum().

This comes at the price of not dual-using `floatMean` anymore, because
we need both `floatMean` and `floatKahanC` for `avg`. This creates a
small overhead, but also makes the Kahan parts easier to read.

The trade-off in terms of performance is similar to the one made in
`sum` with a following division. Things that work out for `sum` thanks
to Kahan summation should equally work out for `avg`. This is
reflected in the added tests.

Signed-off-by: beorn7 <[email protected]>

This expands the idea implemented in #14074, where Kahan summation was
applied to sum().

This comes at the price of not dual-using `floatMean` anymore, because
we need both `floatMean` and `floatKahanC` for `avg`. This creates a
small overhead, but also makes the Kahan parts easier to read.

The trade-off in terms of performance is similar to the one made in
essentially syntactic sugar for `sum` with a following division.
Things that work out for `sum` thanks to Kahan summation should
therefory equally work out for `avg`. This is reflected in the added
tests.

Signed-off-by: beorn7 <[email protected]>

The basic idea here is that the previous code was always doing
incremental calculation of the mean value, which is more costly and
can be less precise. It protects against overflows, but in most cases,
an overflow doesn't happen anyway.

The other idea applied here is to expand on #14074, where Kahan
summation was applied to sum().

With this commit, the average is calculated in a conventional way
(adding everything up and divide in the end) as long as the sum isn't
overflowing float64. This is combined with Kahan summation so that the
avg aggregation, in most cases, is really equivalent to the sum
aggregation with a following division (which is the user's expectation
as avg is supposed to be syntactic sugar for sum with a following
divison).

If the sum hits ±Inf, the calculation reverts to incremental
calculation of the mean value. Kahan summation is also applied here,
although it cannot fully compensate for the numerical errors
introduced by the incremental mean calculation. (The tests added in
this commit would fail if incremental mean calculation was always
used.)

Signed-off-by: beorn7 <[email protected]>

The basic idea here is that the previous code was always doing
incremental calculation of the mean value, which is more costly and
can be less precise. It protects against overflows, but in most cases,
an overflow doesn't happen anyway.

The other idea applied here is to expand on #14074, where Kahan
summation was applied to sum().

With this commit, the average is calculated in a conventional way
(adding everything up and divide in the end) as long as the sum isn't
overflowing float64. This is combined with Kahan summation so that the
avg aggregation, in most cases, is really equivalent to the sum
aggregation with a following division (which is the user's expectation
as avg is supposed to be syntactic sugar for sum with a following
divison).

If the sum hits ±Inf, the calculation reverts to incremental
calculation of the mean value. Kahan summation is also applied here,
although it cannot fully compensate for the numerical errors
introduced by the incremental mean calculation. (The tests added in
this commit would fail if incremental mean calculation was always
used.)

Signed-off-by: beorn7 <[email protected]>

The basic idea here is that the previous code was always doing
incremental calculation of the mean value, which is more costly and
can be less precise. It protects against overflows, but in most cases,
an overflow doesn't happen anyway.

The other idea applied here is to expand on #14074, where Kahan
summation was applied to sum().

With this commit, the average is calculated in a conventional way
(adding everything up and divide in the end) as long as the sum isn't
overflowing float64. This is combined with Kahan summation so that the
avg aggregation, in most cases, is really equivalent to the sum
aggregation with a following division (which is the user's expectation
as avg is supposed to be syntactic sugar for sum with a following
divison).

If the sum hits ±Inf, the calculation reverts to incremental
calculation of the mean value. Kahan summation is also applied here,
although it cannot fully compensate for the numerical errors
introduced by the incremental mean calculation. (The tests added in
this commit would fail if incremental mean calculation was always
used.)

Signed-off-by: beorn7 <[email protected]>

The basic idea here is that the previous code was always doing
incremental calculation of the mean value, which is more costly and
can be less precise. It protects against overflows, but in most cases,
an overflow doesn't happen anyway.

The other idea applied here is to expand on #14074, where Kahan
summation was applied to sum().

With this commit, the average is calculated in a conventional way
(adding everything up and divide in the end) as long as the sum isn't
overflowing float64. This is combined with Kahan summation so that the
avg aggregation, in most cases, is really equivalent to the sum
aggregation with a following division (which is the user's expectation
as avg is supposed to be syntactic sugar for sum with a following
divison).

If the sum hits ±Inf, the calculation reverts to incremental
calculation of the mean value. Kahan summation is also applied here,
although it cannot fully compensate for the numerical errors
introduced by the incremental mean calculation. (The tests added in
this commit would fail if incremental mean calculation was always
used.)

Signed-off-by: beorn7 <[email protected]>

The basic idea here is that the previous code was always doing
incremental calculation of the mean value, which is more costly and
can be less precise. It protects against overflows, but in most cases,
an overflow doesn't happen anyway.

The other idea applied here is to expand on #14074, where Kahan
summation was applied to sum().

With this commit, the average is calculated in a conventional way
(adding everything up and divide in the end) as long as the sum isn't
overflowing float64. This is combined with Kahan summation so that the
avg aggregation, in most cases, is really equivalent to the sum
aggregation with a following division (which is the user's expectation
as avg is supposed to be syntactic sugar for sum with a following
divison).

If the sum hits ±Inf, the calculation reverts to incremental
calculation of the mean value. Kahan summation is also applied here,
although it cannot fully compensate for the numerical errors
introduced by the incremental mean calculation. (The tests added in
this commit would fail if incremental mean calculation was always
used.)

Signed-off-by: beorn7 <[email protected]>

Implements the improvements from
prometheus/prometheus#14074
prometheus/prometheus#14362

* MQE: Use kahan in sum aggregation

Implements the improvements from
prometheus/prometheus#14074
prometheus/prometheus#14362

* Update CHANGELOG