MetricSampleQuantiles (Apache HBase 1.2.12 API)

java.lang.Object
- org.apache.hadoop.metrics2.util.MetricSampleQuantiles

```
@InterfaceAudience.Private
public class MetricSampleQuantiles
extends Object
```
Implementation of the Cormode, Korn, Muthukrishnan, and Srivastava algorithm for streaming calculation of targeted high-percentile epsilon-approximate quantiles. This is a generalization of the earlier work by Greenwald and Khanna (GK), which essentially allows different error bounds on the targeted quantiles, which allows for far more efficient calculation of high-percentiles. See: Cormode, Korn, Muthukrishnan, and Srivastava "Effective Computation of Biased Quantiles over Data Streams" in ICDE 2005 Greenwald and Khanna, "Space-efficient online computation of quantile summaries" in SIGMOD 2001

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`private static class`	`MetricSampleQuantiles.SampleItem` Describes a measured value passed to the estimator, tracking additional metadata required by the CKMS algorithm.

Field Summary

Fields
Modifier and Type	Field and Description
`private long[]`	`buffer` Buffers incoming items to be inserted in batch.
`private int`	`bufferCount`
`private long`	`count` Total number of items in stream
`private MetricQuantile[]`	`quantiles` Array of Quantiles that we care about, along with desired error.
`private LinkedList<MetricSampleQuantiles.SampleItem>`	`samples` Current list of sampled items, maintained in sorted order with error bounds

Constructor Summary

Constructors
Constructor and Description

MetricSampleQuantiles(MetricQuantile[] quantiles)

Constructors
Constructor and Description
`MetricSampleQuantiles(MetricQuantile[] quantiles)`

Method Summary

Methods
Modifier and Type	Method and Description
`private double`	`allowableError(int rank)` Specifies the allowable error for this rank, depending on which quantiles are being targeted.
`void`	`clear()` Resets the estimator, clearing out all previously inserted items
`private void`	`compress()` Try to remove extraneous items from the set of sampled items.
`long`	`getCount()` Returns the number of items that the estimator has processed
`int`	`getSampleCount()` Returns the number of samples kept by the estimator
`void`	`insert(long v)` Add a new value from the stream.
`private void`	`insertBatch()` Merges items from buffer into the samples array in one pass.
`private long`	`query(double quantile)` Get the estimated value at the specified quantile.
`Map<MetricQuantile,Long>`	`snapshot()` Get a snapshot of the current values of all the tracked quantiles.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - count
```
private long count
```
    Total number of items in stream
  - samples
```
private LinkedList<MetricSampleQuantiles.SampleItem> samples
```
    Current list of sampled items, maintained in sorted order with error bounds
  - buffer
```
private long[] buffer
```
    Buffers incoming items to be inserted in batch. Items are inserted into the buffer linearly. When the buffer fills, it is flushed into the samples array in its entirety.
  - bufferCount
```
private int bufferCount
```
  - quantiles
```
private final MetricQuantile[] quantiles
```
    Array of Quantiles that we care about, along with desired error.
- Constructor Detail
  - MetricSampleQuantiles
```
public MetricSampleQuantiles(MetricQuantile[] quantiles)
```
- Method Detail
  - allowableError
```
private double allowableError(int rank)
```
    Specifies the allowable error for this rank, depending on which quantiles are being targeted. This is the f(r_i, n) function from the CKMS paper. It's basically how wide the range of this rank can be.
    
    Parameters:
    rank - the index in the list of samples
  - insert
```
public void insert(long v)
```
    Add a new value from the stream.
    
    Parameters:
    v -
  - insertBatch
```
private void insertBatch()
```
    Merges items from buffer into the samples array in one pass. This is more efficient than doing an insert on every item.
  - compress
```
private void compress()
```
    Try to remove extraneous items from the set of sampled items. This checks if an item is unnecessary based on the desired error bounds, and merges it with the adjacent item if it is.
  - query
```
private long query(double quantile)
            throws IOException
```
    Get the estimated value at the specified quantile.
    
    Parameters:
    quantile - Queried quantile, e.g. 0.50 or 0.99.
    
    Returns:
    Estimated value at that quantile.
    
    Throws:
    
    IOException
  - snapshot
```
public Map<MetricQuantile,Long> snapshot()
                                  throws IOException
```
    Get a snapshot of the current values of all the tracked quantiles.
    
    Returns:
    snapshot of the tracked quantiles
    
    Throws:
    
    IOException - if no items have been added to the estimator
  - getCount
```
public long getCount()
```
    Returns the number of items that the estimator has processed
    
    Returns:
    count total number of items processed
  - getSampleCount
```
public int getSampleCount()
```
    Returns the number of samples kept by the estimator
    
    Returns:
    count current number of samples
  - clear
```
public void clear()
```
    Resets the estimator, clearing out all previously inserted items

Class MetricSampleQuantiles

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

count

samples

buffer

bufferCount

quantiles

Constructor Detail

MetricSampleQuantiles

Method Detail

allowableError

insert

insertBatch

compress

query

snapshot

getCount

getSampleCount

clear