Support Long/Float dimensions with generic dimension handling interface

benchmarks/src/main/java/io/druid/benchmark/QueryableIndexLoadingBenchmark.java

@@ -0,0 +1,311 @@
+/*
+ * Licensed to Metamarkets Group Inc. (Metamarkets) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. Metamarkets 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 io.druid.benchmark;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.google.common.collect.ImmutableMap;
+import com.google.common.collect.Lists;
+import com.google.common.primitives.Ints;
+import com.metamx.common.guava.Sequence;
+import com.metamx.common.guava.Sequences;
+import io.druid.data.input.InputRow;
+import io.druid.data.input.MapBasedInputRow;
+import io.druid.granularity.QueryGranularity;
+import io.druid.jackson.DefaultObjectMapper;
+import io.druid.query.aggregation.AggregatorFactory;
+import io.druid.query.aggregation.CountAggregatorFactory;
+import io.druid.query.aggregation.DoubleSumAggregatorFactory;
+import io.druid.query.aggregation.LongSumAggregatorFactory;
+import io.druid.query.dimension.DefaultDimensionSpec;
+import io.druid.segment.Cursor;
+import io.druid.segment.DimensionSelector;
+import io.druid.segment.IndexIO;
+import io.druid.segment.IndexMergerV9;
+import io.druid.segment.IndexSpec;
+import io.druid.segment.QueryableIndex;
+import io.druid.segment.QueryableIndexStorageAdapter;
+import io.druid.segment.StorageAdapter;
+import io.druid.segment.column.ColumnConfig;
+import io.druid.segment.data.IndexedInts;
+import io.druid.segment.incremental.IncrementalIndex;
+import io.druid.segment.incremental.OnheapIncrementalIndex;
+import org.joda.time.Interval;
+import org.openjdk.jmh.annotations.Benchmark;
+import org.openjdk.jmh.annotations.BenchmarkMode;
+import org.openjdk.jmh.annotations.Level;
+import org.openjdk.jmh.annotations.Mode;
+import org.openjdk.jmh.annotations.OperationsPerInvocation;
+import org.openjdk.jmh.annotations.OutputTimeUnit;
+import org.openjdk.jmh.annotations.Scope;
+import org.openjdk.jmh.annotations.Setup;
+import org.openjdk.jmh.annotations.State;
+import org.openjdk.jmh.annotations.TearDown;
+import org.openjdk.jmh.infra.Blackhole;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+import java.util.Random;
+import java.util.concurrent.TimeUnit;
+
+@State(Scope.Thread)
+public class QueryableIndexLoadingBenchmark
+{
+  private class IndexedIntsDimensionSelectorRow implements Iterable<Comparable>
+  {
+    private final IndexedInts delegate;
+
+    public IndexedIntsDimensionSelectorRow(IndexedInts delegate)
+    {
+      this.delegate = delegate;
+    }
+
+    public Iterator iterator()
+    {
+      return delegate.iterator();
+    }
+
+    public int size()
+    {
+      return delegate.size();
+    }
+
+    public Comparable get(int index)
+    {
+      return delegate.get(index);
+    }
+
+    public IndexedInts getDelegate()
+    {
+      return delegate;
+    }
+  }
+
+  static final int ROW_SIZE = 64;
+  static final int MAX_ROWS = 250000;
+  static final int MAX_VALUE = 20000;
+
+  private static final IndexMergerV9 INDEX_MERGER_V9;
+  private static final IndexIO INDEX_IO;
+  public static final ObjectMapper JSON_MAPPER;
+
+  private QueryableIndex qIndex;
+
+  private static AggregatorFactory[] AGGS;
+
+  private Random rng;
+  private Random rng2;
+  private File tmpFile;
+
+  private StorageAdapter qAdapter;
+
+  private boolean initialized = false;
+
+  static {
+    JSON_MAPPER = new DefaultObjectMapper();
+    INDEX_IO = new IndexIO(
+        JSON_MAPPER,
+        new ColumnConfig()
+        {
+          @Override
+          public int columnCacheSizeBytes()
+          {
+            return 0;
+          }
+        }
+    );
+    INDEX_MERGER_V9 = new IndexMergerV9(JSON_MAPPER, INDEX_IO);
+  }
+
+  static {
+    final ArrayList<AggregatorFactory> ingestAggregatorFactories = new ArrayList<>(ROW_SIZE + 1);
+    ingestAggregatorFactories.add(new CountAggregatorFactory("rows"));
+    for (int i = 0; i < ROW_SIZE; ++i) {
+      if (i % 2 != 0) {
+        continue;
+      }
+
+      ingestAggregatorFactories.add(
+          new LongSumAggregatorFactory(
+              String.format("sumResult%s", i),
+              String.format("Dim_%s", i)
+          )
+      );
+      ingestAggregatorFactories.add(
+          new DoubleSumAggregatorFactory(
+              String.format("doubleSumResult%s", i),
+              String.format("Dim_%s", i)
+          )
+      );
+    }
+    AGGS = ingestAggregatorFactories.toArray(new AggregatorFactory[0]);
+  }
+
+  @Setup
+  public void setup() throws IOException
+  {
+    if(initialized) {
+      return;
+    }
+
+    rng = new Random(9999);
+    rng2 = new Random(9999);
+
+    IncrementalIndex index = makeIncIndex();
+
+    File tmpFile = File.createTempFile("IndexedIntsWrappingBenchmark-INDEX-", String.valueOf(rng.nextInt(10)));
+    tmpFile.delete();
+    tmpFile.mkdirs();
+    System.out.println(tmpFile.getAbsolutePath() + " isFile: " + tmpFile.isFile() + " isDir:" + tmpFile.isDirectory());
+    tmpFile.deleteOnExit();
+    File indexFile = INDEX_MERGER_V9.persist(
+        index,
+        tmpFile,
+        new IndexSpec()
+    );
+
+    index.close();
+    index = null;
+
+    // Uncomment this to use a fixed location.
+    //File indexFile = new File("/Users/user/bench_index/64dim_card20000");
+
+    if (qIndex != null) {
+      qIndex.close();
+    }
+    qIndex = makeQueryableIndexFromFile(indexFile);
+    qAdapter = new QueryableIndexStorageAdapter(qIndex);
+    initialized = true;
+  }
+
+  private IncrementalIndex makeIncIndex() throws IOException
+  {
+    IncrementalIndex index = new OnheapIncrementalIndex(
+        0,
+        QueryGranularity.NONE,
+        AGGS,
+        false,
+        true,
+        true,
+        MAX_ROWS
+    );
+
+    for (int i = 0; i < MAX_ROWS; i++) {
+      InputRow row = getStringRow(5L, i, ROW_SIZE);
+      index.add(row);
+    }
+
+    return index;
+  }
+
+  private QueryableIndex makeQueryableIndex(IncrementalIndex incIndex) throws IOException
+  {
+    QueryableIndex index =
+        INDEX_IO.loadIndex(
+            INDEX_MERGER_V9.persist(
+                incIndex,
+                tmpFile,
+                new IndexSpec()
+            )
+        );
+    return index;
+  }
+
+  private QueryableIndex makeQueryableIndexFromFile(File file) throws IOException
+  {
+    QueryableIndex index =
+        INDEX_IO.loadIndex(file);
+    return index;
+  }
+
+  private MapBasedInputRow getStringRow(long timestamp, int rowID, int dimensionCount)
+  {
+    List<String> dimensionList = new ArrayList<String>(dimensionCount);
+    ImmutableMap.Builder<String, Object> builder = ImmutableMap.builder();
+    for (int i = 0; i < dimensionCount; i++) {
+      String dimName = String.format("Dim_%d", i);
+      dimensionList.add(dimName);
+      builder.put(dimName, String.valueOf(rng2.nextInt(MAX_VALUE)));
+    }
+    return new MapBasedInputRow(timestamp, dimensionList, builder.build());
+  }
+
+  @Benchmark
+  @BenchmarkMode(Mode.AverageTime)
+  @OutputTimeUnit(TimeUnit.MICROSECONDS)
+  @OperationsPerInvocation(MAX_ROWS)
+  public void AloadQIndex(Blackhole blackhole) throws Exception
+  {
+    int sum = 0;
+    Sequence<Cursor> qCursors = qAdapter.makeCursors(null, new Interval(0L, 10L), QueryGranularity.NONE, false);
+    Cursor qCursor = Sequences.toList(Sequences.limit(qCursors, 1), Lists.<Cursor>newArrayList()).get(0);
+    qCursor.reset();
+    List<DimensionSelector> qSelectors = new ArrayList<>();
+    for (int i = 0; i < ROW_SIZE; i++) {
+      String dimName = String.format("Dim_%s", i);
+      qSelectors.add(qCursor.makeDimensionSelector(new DefaultDimensionSpec(dimName, dimName)));
+    }
+
+    for (int i = 0; i < MAX_ROWS; i++) {
+      for (int j = 0; j < ROW_SIZE; j++) {
+        IndexedInts row = qSelectors.get(j).getRow();
+        for (int val : row) {
+          sum += val;
+          blackhole.consume(val);
+        }
+      }
+      qCursor.advance();
+    }
+    blackhole.consume(sum);
+  }
+
+  @Benchmark
+  @BenchmarkMode(Mode.AverageTime)
+  @OutputTimeUnit(TimeUnit.MICROSECONDS)
+  @OperationsPerInvocation(MAX_ROWS)
+  public void loadQIndexWrap(Blackhole blackhole) throws Exception
+  {
+    int sum = 0;
+    Sequence<Cursor> qCursors = qAdapter.makeCursors(null, new Interval(0L, 10L), QueryGranularity.NONE, false);
+    Cursor qCursor = Sequences.toList(Sequences.limit(qCursors, 1), Lists.<Cursor>newArrayList()).get(0);
+    qCursor.reset();
+    List<DimensionSelector> qSelectors = new ArrayList<>();
+    for (int i = 0; i < ROW_SIZE; i++) {
+      String dimName = String.format("Dim_%s", i);
+      qSelectors.add(qCursor.makeDimensionSelector(new DefaultDimensionSpec(dimName, dimName)));
+    }
+
+    for (int i = 0; i < MAX_ROWS; i++) {
+      for (int j = 0; j < ROW_SIZE; j++) {
+        IndexedInts row = qSelectors.get(j).getRow();
+        IndexedIntsDimensionSelectorRow dsRow = new IndexedIntsDimensionSelectorRow(row);
+        for (Comparable val : dsRow) {
+          sum += (Integer) val;
+          blackhole.consume(val);
+        }
+      }
+      qCursor.advance();
+    }
+    blackhole.consume(sum);
+  }
+
+}

docs/content/design/index.md

@@ -24,7 +24,7 @@ This data set is composed of three distinct components. If you are acquainted wi
 * **Timestamp column**: We treat timestamp separately because all of our queries
  center around the time axis.
 
-* **Dimension columns**: Dimensions are string attributes of an event, and the columns most commonly used in filtering the data. 
+* **Dimension columns**: Dimensions are attributes of an event, the columns most commonly used in filtering the data. 
 We have four dimensions in our example data set: publisher, advertiser, gender, and country.
 They each represent an axis of the data that we’ve chosen to slice across.
 

docs/content/design/segments.md

@@ -30,16 +30,15 @@ image below:
 
 ![Druid column types](../../img/druid-column-types.png "Druid Column Types")
 
-The timestamp and metric columns are simple: behind the scenes each of
+The timestamp, numeric dimension (i.e. Long and Float), and metric columns are simple: behind the scenes each of
 these is an array of integer or floating point values compressed with
 LZ4. Once a query knows which rows it needs to select, it simply
 decompresses these, pulls out the relevant rows, and applies the
 desired aggregation operator. As with all columns, if a query doesn’t
 require a column, then that column’s data is just skipped over.
 
-Dimensions columns are different because they support filter and
-group-by operations, so each dimension requires the following
-three data structures:
+String-typed dimension columns have additional data structures for supporting filter and
+group-by operations:
 
 1. A dictionary that maps values (which are always treated as strings) to integer IDs,
 2. A list of the column’s values, encoded using the dictionary in 1, and
@@ -88,6 +87,9 @@ columns will have extremely sparse, and therefore highly compressible,
 bitmaps. Druid exploits this using compression algorithms that are
 specially suited for bitmaps, such as roaring bitmap compression.
 
+Numeric dimensions do not currently have any supporting index structures, so filtering operations
+on such dimensions require a full scan.
+
 ### Multi-value columns
 
 If a data source makes use of multi-value columns, then the data

docs/content/development/geo.md

@@ -5,6 +5,10 @@ layout: doc_page
 Druid supports filtering specially spatially indexed columns based on an origin and a bound.
 
 # Spatial Indexing
+<div class="note caution">
+@Deprecated.
+In the dataSpec, the spatial dimensions should be specified within the "dimensions" list instead.
+</div>
 In any of the data specs, there is the option of providing spatial dimensions. For example, for a JSON data spec, spatial dimensions can be specified as follows:
 
 ```json
@@ -21,6 +25,8 @@ In any of the data specs, there is the option of providing spatial dimensions. F
 }
 ```
 
+
+
 |property|description|required?|
 |--------|-----------|---------|
 |dimName|The name of the spatial dimension. A spatial dimension may be constructed from multiple other dimensions or it may already exist as part of an event. If a spatial dimension already exists, it must be an array of coordinate values.|yes|

docs/content/ingestion/faq.md

@@ -16,7 +16,7 @@ If you are trying to batch load historical data but no events are being loaded,
 
 ## What types of data does Druid support?
 
-Druid can ingest JSON, CSV, TSV and other delimited data out of the box. Druid supports single dimension values, or multiple dimension values (an array of strings). Druid supports long and float numeric columns.
+Druid can ingest JSON, CSV, TSV and other delimited data out of the box. Druid supports single dimension values, or multiple dimension values (an array of values). Druid supports long and float numeric columns.
 
 ## Not all of my events were ingested