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* Licensed to the Apache Software Foundation (ASF) under one
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* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.cassandra.io.sstable;
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Collections;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.cassandra.cache.RefCountedMemory;
import org.apache.cassandra.db.DecoratedKey;
import org.apache.cassandra.db.TypeSizes;
import org.apache.cassandra.dht.IPartitioner;
import static org.apache.cassandra.io.sstable.Downsampling.BASE_SAMPLING_LEVEL;
import static org.apache.cassandra.io.sstable.SSTable.getMinimalKey;
public class IndexSummaryBuilder
{
private static final Logger logger = LoggerFactory.getLogger(IndexSummaryBuilder.class);
private final ArrayList<Long> positions;
private final ArrayList<DecoratedKey> keys;
private final int minIndexInterval;
private final int samplingLevel;
private final int[] startPoints;
private long keysWritten = 0;
private long indexIntervalMatches = 0;
private long offheapSize = 0;
public IndexSummaryBuilder(long expectedKeys, int minIndexInterval, int samplingLevel)
{
this.samplingLevel = samplingLevel;
this.startPoints = Downsampling.getStartPoints(BASE_SAMPLING_LEVEL, samplingLevel);
long maxExpectedEntries = expectedKeys / minIndexInterval;
if (maxExpectedEntries > Integer.MAX_VALUE)
{
// that's a _lot_ of keys, and a very low min index interval
int effectiveMinInterval = (int) Math.ceil((double) Integer.MAX_VALUE / expectedKeys);
maxExpectedEntries = expectedKeys / effectiveMinInterval;
assert maxExpectedEntries <= Integer.MAX_VALUE : maxExpectedEntries;
logger.warn("min_index_interval of {} is too low for {} expected keys; using interval of {} instead",
minIndexInterval, expectedKeys, effectiveMinInterval);
this.minIndexInterval = effectiveMinInterval;
}
else
{
this.minIndexInterval = minIndexInterval;
}
// for initializing data structures, adjust our estimates based on the sampling level
maxExpectedEntries = (maxExpectedEntries * samplingLevel) / BASE_SAMPLING_LEVEL;
positions = new ArrayList<>((int)maxExpectedEntries);
keys = new ArrayList<>((int)maxExpectedEntries);
}
// finds the last (-offset) decorated key that can be guaranteed to occur fully in the index file before the provided file position
public DecoratedKey getMaxReadableKey(long position, int offset)
{
int i = Collections.binarySearch(positions, position);
if (i < 0)
{
i = -1 - i;
if (i == positions.size())
i -= 2;
else
i -= 1;
}
else
i -= 1;
i -= offset;
// we don't want to return any key if there's only 1 item in the summary, to make sure the sstable range is non-empty
if (i <= 0)
return null;
return keys.get(i);
}
public IndexSummaryBuilder maybeAddEntry(DecoratedKey decoratedKey, long indexPosition)
{
if (keysWritten % minIndexInterval == 0)
{
// see if we should skip this key based on our sampling level
boolean shouldSkip = false;
for (int start : startPoints)
{
if ((indexIntervalMatches - start) % BASE_SAMPLING_LEVEL == 0)
{
shouldSkip = true;
break;
}
}
if (!shouldSkip)
{
keys.add(getMinimalKey(decoratedKey));
offheapSize += decoratedKey.getKey().remaining();
positions.add(indexPosition);
offheapSize += TypeSizes.NATIVE.sizeof(indexPosition);
}
indexIntervalMatches++;
}
keysWritten++;
return this;
}
public IndexSummary build(IPartitioner partitioner)
{
return build(partitioner, null);
}
public IndexSummary build(IPartitioner partitioner, DecoratedKey exclusiveUpperBound)
{
assert keys.size() > 0;
assert keys.size() == positions.size();
int length;
if (exclusiveUpperBound == null)
length = keys.size();
else
length = Collections.binarySearch(keys, exclusiveUpperBound);
assert length > 0;
long offheapSize = this.offheapSize;
if (length < keys.size())
for (int i = length ; i < keys.size() ; i++)
offheapSize -= keys.get(i).getKey().remaining() + TypeSizes.NATIVE.sizeof(positions.get(i));
// first we write out the position in the *summary* for each key in the summary,
// then we write out (key, actual index position) pairs
RefCountedMemory memory = new RefCountedMemory(offheapSize + (length * 4));
int idxPosition = 0;
int keyPosition = length * 4;
for (int i = 0; i < length; i++)
{
// write the position of the actual entry in the index summary (4 bytes)
memory.setInt(idxPosition, keyPosition);
idxPosition += TypeSizes.NATIVE.sizeof(keyPosition);
// write the key
ByteBuffer keyBytes = keys.get(i).getKey();
memory.setBytes(keyPosition, keyBytes);
keyPosition += keyBytes.remaining();
// write the position in the actual index file
long actualIndexPosition = positions.get(i);
memory.setLong(keyPosition, actualIndexPosition);
keyPosition += TypeSizes.NATIVE.sizeof(actualIndexPosition);
}
assert keyPosition == offheapSize + (length * 4);
int sizeAtFullSampling = (int) Math.ceil(keysWritten / (double) minIndexInterval);
return new IndexSummary(partitioner, memory, length, sizeAtFullSampling, minIndexInterval, samplingLevel);
}
public static int entriesAtSamplingLevel(int samplingLevel, int maxSummarySize)
{
return (int) Math.ceil((samplingLevel * maxSummarySize) / (double) BASE_SAMPLING_LEVEL);
}
public static int calculateSamplingLevel(int currentSamplingLevel, int currentNumEntries, long targetNumEntries, int minIndexInterval, int maxIndexInterval)
{
// effective index interval == (BASE_SAMPLING_LEVEL / samplingLevel) * minIndexInterval
// so we can just solve for minSamplingLevel here:
// maxIndexInterval == (BASE_SAMPLING_LEVEL / minSamplingLevel) * minIndexInterval
int effectiveMinSamplingLevel = Math.max(1, (int) Math.ceil((BASE_SAMPLING_LEVEL * minIndexInterval) / (double) maxIndexInterval));
// Algebraic explanation for calculating the new sampling level (solve for newSamplingLevel):
// originalNumEntries = (baseSamplingLevel / currentSamplingLevel) * currentNumEntries
// newSpaceUsed = (newSamplingLevel / baseSamplingLevel) * originalNumEntries
// newSpaceUsed = (newSamplingLevel / baseSamplingLevel) * (baseSamplingLevel / currentSamplingLevel) * currentNumEntries
// newSpaceUsed = (newSamplingLevel / currentSamplingLevel) * currentNumEntries
// (newSpaceUsed * currentSamplingLevel) / currentNumEntries = newSamplingLevel
int newSamplingLevel = (int) (targetNumEntries * currentSamplingLevel) / currentNumEntries;
return Math.min(BASE_SAMPLING_LEVEL, Math.max(effectiveMinSamplingLevel, newSamplingLevel));
}
/**
* Downsamples an existing index summary to a new sampling level.
* @param existing an existing IndexSummary
* @param newSamplingLevel the target level for the new IndexSummary. This must be less than the current sampling
* level for `existing`.
* @param partitioner the partitioner used for the index summary
* @return a new IndexSummary
*/
public static IndexSummary downsample(IndexSummary existing, int newSamplingLevel, int minIndexInterval, IPartitioner partitioner)
{
// To downsample the old index summary, we'll go through (potentially) several rounds of downsampling.
// Conceptually, each round starts at position X and then removes every Nth item. The value of X follows
// a particular pattern to evenly space out the items that we remove. The value of N decreases by one each
// round.
int currentSamplingLevel = existing.getSamplingLevel();
assert currentSamplingLevel > newSamplingLevel;
assert minIndexInterval == existing.getMinIndexInterval();
// calculate starting indexes for downsampling rounds
int[] startPoints = Downsampling.getStartPoints(currentSamplingLevel, newSamplingLevel);
// calculate new off-heap size
int removedKeyCount = 0;
long newOffHeapSize = existing.getOffHeapSize();
for (int start : startPoints)
{
for (int j = start; j < existing.size(); j += currentSamplingLevel)
{
removedKeyCount++;
newOffHeapSize -= existing.getEntry(j).length;
}
}
int newKeyCount = existing.size() - removedKeyCount;
// Subtract (removedKeyCount * 4) from the new size to account for fewer entries in the first section, which
// stores the position of the actual entries in the summary.
RefCountedMemory memory = new RefCountedMemory(newOffHeapSize - (removedKeyCount * 4));
// Copy old entries to our new Memory.
int idxPosition = 0;
int keyPosition = newKeyCount * 4;
outer:
for (int oldSummaryIndex = 0; oldSummaryIndex < existing.size(); oldSummaryIndex++)
{
// to determine if we can skip this entry, go through the starting points for our downsampling rounds
// and see if the entry's index is covered by that round
for (int start : startPoints)
{
if ((oldSummaryIndex - start) % currentSamplingLevel == 0)
continue outer;
}
// write the position of the actual entry in the index summary (4 bytes)
memory.setInt(idxPosition, keyPosition);
idxPosition += TypeSizes.NATIVE.sizeof(keyPosition);
// write the entry itself
byte[] entry = existing.getEntry(oldSummaryIndex);
memory.setBytes(keyPosition, entry, 0, entry.length);
keyPosition += entry.length;
}
return new IndexSummary(partitioner, memory, newKeyCount, existing.getMaxNumberOfEntries(),
minIndexInterval, newSamplingLevel);
}
}