## bitcoinleveldb-histogram

This Rust crate is a direct translation from C++
to Rust of the Bitcoin Core histogram utility. It
provides a tool for collecting and analyzing data
for the purpose of creating histograms, which are
a way of visualizing data distributions.

The `Histogram` struct provides various methods
for managing and analyzing histogram data. The
`add` method is used to add a data point to the
histogram, while `clear` removes all data from the
histogram. `average` calculates the average value
of the data points in the histogram, while
`median` calculates the median value. The
`percentile` method returns the value at a given
percentile, and `standard_deviation` calculates
the standard deviation of the data.

One notable feature of the `Histogram` struct is
the `merge` method, which can be used to merge two
histograms together into a single histogram. This
can be useful for analyzing large sets of data
that have been split across multiple histograms.

While there are no explicit mathematical equations
associated with the methods provided by this
crate, they are all related to statistics and data
analysis.

Notice: This crate is part of a direct translation
from C++ to Rust of the Bitcoin Core histogram
utility. Some of the function bodies may still be
in the process of translation. Please note that
this system will become testable once the
translation process is complete.

32> how do we use histogram in leveldb?

Histograms are used in LevelDB to collect and
report various statistics about the performance of
the database. For example, histograms can be used
to track the distribution of read latencies or the
sizes of SSTables.

The `Histogram` class in LevelDB provides a way to
collect and analyze these statistics. The class
provides methods for adding samples to the
histogram, computing percentiles, and computing
summary statistics like the median and standard
deviation.

To use a histogram in LevelDB, you would typically
create an instance of the `Histogram` class and
then add samples to it as necessary. Once you have
collected enough samples, you can use the
histogram methods to analyze the data and compute
summary statistics.

Here's an example of how you might use a histogram
in LevelDB to collect and analyze the read
latencies for a database:

```rust
use bitcoinleveldb_histogram::Histogram;

// Create a new histogram with 1000 buckets
let mut read_latency_hist = Histogram::new(1000);

// Record some read latencies
for i in 0..1000 {
    let start_time = std::time::Instant::now();
    // Perform a read operation
    let end_time = std::time::Instant::now();
    let elapsed_time = end_time.duration_since(start_time).as_micros() as u64;
    read_latency_hist.add(elapsed_time);
}

// Compute the median and 99th percentile latency
let median_latency = read_latency_hist.median();
let p99_latency = read_latency_hist.percentile(99.0);

// Print out some summary statistics
println!("Median read latency: {} us", median_latency);
println!("99th percentile read latency: {} us", p99_latency);
println!("Average read latency: {} us", read_latency_hist.average());
println!("Standard deviation of read latency: {} us", read_latency_hist.standard_deviation());
```

This code creates a new `Histogram` with 1000
buckets, and then records some read latencies by
performing read operations on the database. After
collecting enough samples, it computes the median
and 99th percentile latency, as well as the
average and standard deviation of the latency
distribution. These statistics can be used to gain
insight into the performance of the database and
identify potential performance bottlenecks.