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The Apache Cassandra Project develops a highly scalable second-generation distributed database, bringing together Dynamo's fully distributed design and Bigtable's ColumnFamily-based data model.
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* 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.tools.nodetool;
import static com.google.common.base.Preconditions.checkArgument;
import static java.lang.String.format;
import io.airlift.airline.Arguments;
import io.airlift.airline.Command;
import java.io.PrintStream;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import com.google.common.collect.HashMultimap;
import com.google.common.collect.Multimap;
import org.apache.cassandra.db.ColumnFamilyStoreMBean;
import org.apache.cassandra.metrics.CassandraMetricsRegistry;
import org.apache.cassandra.tools.NodeProbe;
import org.apache.cassandra.tools.NodeTool.NodeToolCmd;
import org.apache.cassandra.utils.EstimatedHistogram;
import org.apache.commons.lang3.ArrayUtils;
@Command(name = "tablehistograms", description = "Print statistic histograms for a given table")
public class TableHistograms extends NodeToolCmd
{
@Arguments(usage = "[ | ]", description = "The keyspace and table name")
private List args = new ArrayList<>();
@Override
public void execute(NodeProbe probe)
{
PrintStream out = probe.output().out;
Multimap tablesList = HashMultimap.create();
// a > mapping for verification or as reference if none provided
Multimap allTables = HashMultimap.create();
Iterator> tableMBeans = probe.getColumnFamilyStoreMBeanProxies();
while (tableMBeans.hasNext())
{
Map.Entry entry = tableMBeans.next();
allTables.put(entry.getKey(), entry.getValue().getTableName());
}
if (args.size() == 2)
{
tablesList.put(args.get(0), args.get(1));
}
else if (args.size() == 1)
{
String[] input = args.get(0).split("\\.");
checkArgument(input.length == 2, "tablehistograms requires keyspace and table name arguments");
tablesList.put(input[0], input[1]);
}
else
{
// use all tables
tablesList = allTables;
}
// verify that all tables to list exist
for (String keyspace : tablesList.keys())
{
for (String table : tablesList.get(keyspace))
{
if (!allTables.containsEntry(keyspace, table))
throw new IllegalArgumentException("Unknown table " + keyspace + '.' + table);
}
}
for (String keyspace : tablesList.keys())
{
for (String table : tablesList.get(keyspace))
{
// calculate percentile of row size and column count
long[] estimatedPartitionSize = (long[]) probe.getColumnFamilyMetric(keyspace, table, "EstimatedPartitionSizeHistogram");
long[] estimatedColumnCount = (long[]) probe.getColumnFamilyMetric(keyspace, table, "EstimatedColumnCountHistogram");
// build arrays to store percentile values
double[] estimatedRowSizePercentiles = new double[7];
double[] estimatedColumnCountPercentiles = new double[7];
double[] offsetPercentiles = new double[]{0.5, 0.75, 0.95, 0.98, 0.99};
if (ArrayUtils.isEmpty(estimatedPartitionSize) || ArrayUtils.isEmpty(estimatedColumnCount))
{
out.println("No SSTables exists, unable to calculate 'Partition Size' and 'Cell Count' percentiles");
for (int i = 0; i < 7; i++)
{
estimatedRowSizePercentiles[i] = Double.NaN;
estimatedColumnCountPercentiles[i] = Double.NaN;
}
}
else
{
EstimatedHistogram partitionSizeHist = new EstimatedHistogram(estimatedPartitionSize);
EstimatedHistogram columnCountHist = new EstimatedHistogram(estimatedColumnCount);
if (partitionSizeHist.isOverflowed())
{
out.println(String.format("Row sizes are larger than %s, unable to calculate percentiles", partitionSizeHist.getLargestBucketOffset()));
for (int i = 0; i < offsetPercentiles.length; i++)
estimatedRowSizePercentiles[i] = Double.NaN;
}
else
{
for (int i = 0; i < offsetPercentiles.length; i++)
estimatedRowSizePercentiles[i] = partitionSizeHist.percentile(offsetPercentiles[i]);
}
if (columnCountHist.isOverflowed())
{
out.println(String.format("Column counts are larger than %s, unable to calculate percentiles", columnCountHist.getLargestBucketOffset()));
for (int i = 0; i < estimatedColumnCountPercentiles.length; i++)
estimatedColumnCountPercentiles[i] = Double.NaN;
}
else
{
for (int i = 0; i < offsetPercentiles.length; i++)
estimatedColumnCountPercentiles[i] = columnCountHist.percentile(offsetPercentiles[i]);
}
// min value
estimatedRowSizePercentiles[5] = partitionSizeHist.min();
estimatedColumnCountPercentiles[5] = columnCountHist.min();
// max value
estimatedRowSizePercentiles[6] = partitionSizeHist.max();
estimatedColumnCountPercentiles[6] = columnCountHist.max();
}
String[] percentiles = new String[]{"50%", "75%", "95%", "98%", "99%", "Min", "Max"};
Double[] readLatency = probe.metricPercentilesAsArray((CassandraMetricsRegistry.JmxTimerMBean) probe.getColumnFamilyMetric(keyspace, table, "ReadLatency"));
Double[] writeLatency = probe.metricPercentilesAsArray((CassandraMetricsRegistry.JmxTimerMBean) probe.getColumnFamilyMetric(keyspace, table, "WriteLatency"));
Double[] sstablesPerRead = probe.metricPercentilesAsArray((CassandraMetricsRegistry.JmxHistogramMBean) probe.getColumnFamilyMetric(keyspace, table, "SSTablesPerReadHistogram"));
out.println(format("%s/%s histograms", keyspace, table));
out.println(format("%-10s%18s%18s%18s%18s%18s",
"Percentile", "Read Latency", "Write Latency", "SSTables", "Partition Size", "Cell Count"));
out.println(format("%-10s%18s%18s%18s%18s%18s",
"", "(micros)", "(micros)", "", "(bytes)", ""));
for (int i = 0; i < percentiles.length; i++)
{
out.println(format("%-10s%18.2f%18.2f%18.2f%18.0f%18.0f",
percentiles[i],
readLatency[i],
writeLatency[i],
sstablesPerRead[i],
estimatedRowSizePercentiles[i],
estimatedColumnCountPercentiles[i]));
}
out.println();
}
}
}
}
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