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# Metrics

* Status: proposed
* Deciders: WasmX
* Date: 2024-05-03

## Table of Contents

## Problem Statement

Support definition, update and retrieval of metrics from Proxy-Wasm filters,
ngx_wasm_module itself and Lua land. How exactly are metrics stored and how
access to them is coordinated to ensure two Nginx workers never write to the
same memory space?

## Technical Context

A metric can be either a counter, a gauge or a histogram.
A counter is an integer that can only be increased.
A gauge is an integer that can take arbitrary positive values.

A histogram, used to represent ranges' frequency of a variable, can be defined
as a set of pairs of range and counter. For example, the distribution of the
response time of an HTTP requests' group, can be represented as a histogram with
ranges `[0, 10]`, `(10, 100]` and `(100, Inf]`. The 1st range's counter, would
be the number of requests whose response time <= 10ms; the 2nd range's counter,
requests whose 10ms < response time <= l00ms; and the last range's counter,
requests whose response time > 100ms.

A metric's value should reflect updates from all worker processes. If a counter
is `0`, after being incremented by workers 0 and 1, it should be `2` -- despite
the worker it's retrieved from. A gauge, however, is whatever value last set by
any of the workers. Histograms, like counters, account for values recorded by
all workers.

## Decision Drivers

* Full Proxy-Wasm ABI compatibility
* Build atop ngx_wasm_shm
* Minimize memory usage
* Minimize metrics access cost

## Proposal

The proposed scheme for metrics storage builds atop ngx_wasm_shm's key-value
store. Metric name is stored as a key in a red-black tree node along with metric
value. Metric value is represented by `ngx_wa_metric_t`, see below. The member
`type` is the metric type while the flexible array member `slots`, stores actual
metric data.

The length of `slots` equals the number of worker processes running when the
metric is defined. This ensures each worker has its own dedicated slot to write
metric updates.

For counters, each entry in the `slots` array is simply an unsigned integer that
its assigned worker increments. When a counter is retrieved, the values in the
`slots` array are then summed and returned.

For gauges, each of the `slots` is a pair of unsigned integer and timestamp.
When a worker sets a gauge, the value is stored along with the time
it's being updated in its slot. When a gauge is retrieved, the values in the
`slots` are iterated and the most recent value is returned.

For histograms, each of the `slots` points to a `ngx_wa_metrics_histogram_t`
instance. Each worker updates the histogram pointed to by its slot. When a
histogram is retrieved, the `slots` array is iterated and each worker's
histogram is merged into a temporary histogram, which can then be serialized.

```c
typedef enum {
NGX_WA_METRIC_COUNTER,
NGX_WA_METRIC_GAUGE,
NGX_WA_METRIC_HISTOGRAM,
} ngx_wa_metric_type_e;


typedef struct {
ngx_uint_t value;
ngx_msec_t last_update;
} ngx_wa_metrics_gauge_t;


typedef struct {
uint32_t upper_bound;
uint32_t count;
} ngx_wa_metrics_bin_t;


typedef struct {
uint8_t n_bins;
ngx_wa_metrics_bin_t bins[];
} ngx_wa_metrics_histogram_t;


typedef union {
ngx_uint_t counter;
ngx_wa_metrics_gauge_t gauge;
ngx_wa_metrics_histogram_t *histogram;
} ngx_wa_metric_val_t;


typedef struct {
ngx_wa_metric_type_e type;
ngx_wa_metric_val_t slots[];
} ngx_wa_metric_t;
```

This storage strategy ensures that two workers **never** write to the same
memory address when updating a metric as long as no memory allocation is
performed. This is indeed the case for counters and gauges and it's also the
case for most histogram updates.

This is an important feature of this design as it allows the more frequent
update operations to be performed without the aid of a locks. The cost of a
lock-less metric update is merely the cost of searching the key-value store red
black tree, O(logn).

The capacity of updating a metric without acquiring a lock is particularly
attractive when a set of worker processes is under heavy load. In such
conditions, lock contention is likely to impact proxy throughput as workers are
more likely to wait for a lock to be released before proceeding with its metric
update and resume its workload.

Metric definition and removal still require locks to be safely performed as two
workers might end up attempting to write to the same memory location. This is
also true for histogram updates which cause them to grow in number of `bins`.

The ABI proposed to accomplish the described system closely resembles the one
from Proxy-Wasm specification itself:

```c
ngx_int_t ngx_wa_metrics_add(ngx_wa_metrics_t *metrics, ngx_str_t *name,
ngx_wa_metric_type_e type, uint32_t *out);
ngx_int_t ngx_wa_metrics_get(ngx_wa_metrics_t *metrics, uint32_t metric_id,
ngx_uint_t *out);
ngx_int_t ngx_wa_metrics_increment(ngx_wa_metrics_t *metrics,
uint32_t metric_id, ngx_int_t val);
ngx_int_t ngx_wa_metrics_record(ngx_wa_metrics_t *metrics, uint32_t metric_id,
ngx_int_t val);
```
### Histograms
This proposal includes a scheme composed of `ngx_wa_metrics_bin_t`, a pair of
upper bound and counter, and `ngx_wa_metrics_histogram_t`, a list of
`ngx_wa_metrics_bin_t` ordered by upper bound, to represent histogram data in
memory. A bin's counter is the number of recorded values less than or equal to
its upper bound and bigger than the previous bin's upper bound.
This storage layout can represent both histograms with user-defined bins and
those following an automatic binning strategy, like logarithmic binning. This
document will focus, however, on logarithmic binning; user-defined bins are left
for a future iteration.
#### Binning
The proposed binning strategy assumes the domain of the variables being measured
is the set of nonnegative integers and divides this domain into bins whose upper
bound grows in powers of 2, i.e., 1, 2, 4, 8, 16, etc. The mapping of a value
`v` to its bin is given by the function `pow(2, ceil(log2(v)))` which calculates
the bin's upper bound. The value 10, for example, is mapped to the bin whose
upper bound is `pow(2, ceil(log2(10)))`, or 16. The bin with upper bound `16`
represents recorded values between `8` and `16`.
This logarithmic scaling provides good enough resolution for small values in
return for low resolution for large values while keeping the memory footprint
reasonably low: values up to 65,536 can be represented with only 16 bins. These
characteristics fit the typical use case of measuring HTTP response time in
milliseconds.
#### Allocation and update handling
Histograms are created with enough space for 5 bins, one of which is initialized
with NGX_MAX_UINT32_VALUE as upper bound, leaving 4 uninitialized.
If a value v is recorded into a histogram and its respective bin is part of the
histogram's bins, its counter is simply incremented. If not, and there's at
least one uninitialized bin, then one bin is initialized with v's upper bound,
the bins are rearranged to ensure ascending order with respect to upper bound,
and the new bin's counter is finally incremented.
#### Growth
If a value v is recorded but its bin isn't part of the histogram's bins and
there aren't any uninitialized bins left, the histogram needs to grow to
accommodate the new value's bin.
Growing a histogram means allocating memory for a new histogram instance with
enough space for the additional bin, copying memory from the old instance to
the new one finally releasing the memory occupied by the old histogram. The new
uninitialized bin is then initialized with v's upper bound and its counter is
incremented.
Histograms, however, can only grow up to a maximum number of bins. When a value
`v` is recorded into a histogram, but its bin isn't part of the bins and the
histogram's reached the bin limit, the bin with the smallest upper bound bigger
than `v` is incremented.

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