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From paul-rogers <...@git.apache.org>
Subject [GitHub] drill pull request #729: Drill 1328: Support table statistics for Parquet
Date Sun, 12 Feb 2017 01:51:23 GMT
Github user paul-rogers commented on a diff in the pull request:

    https://github.com/apache/drill/pull/729#discussion_r100681239
  
    --- Diff: exec/java-exec/src/main/java/org/apache/drill/exec/planner/common/DrillStatsTable.java
---
    @@ -0,0 +1,347 @@
    +/**
    + * 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
    + * <p/>
    + * http://www.apache.org/licenses/LICENSE-2.0
    + * <p/>
    + * 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.drill.exec.planner.common;
    +
    +import java.io.IOException;
    +import java.util.List;
    +import java.util.Map;
    +import java.util.concurrent.TimeUnit;
    +import com.fasterxml.jackson.annotation.JsonIgnore;
    +import com.fasterxml.jackson.annotation.JsonGetter;
    +import com.fasterxml.jackson.annotation.JsonSetter;
    +import com.fasterxml.jackson.annotation.JsonProperty;
    +import com.fasterxml.jackson.annotation.JsonSubTypes;
    +import com.fasterxml.jackson.annotation.JsonTypeInfo;
    +import com.fasterxml.jackson.annotation.JsonTypeName;
    +import com.fasterxml.jackson.databind.DeserializationFeature;
    +import com.fasterxml.jackson.databind.ObjectMapper;
    +import com.google.common.base.Stopwatch;
    +import com.google.common.collect.Maps;
    +import org.apache.calcite.rel.RelNode;
    +import org.apache.calcite.rel.RelVisitor;
    +import org.apache.calcite.rel.core.TableScan;
    +import org.apache.drill.common.exceptions.DrillRuntimeException;
    +import org.apache.drill.exec.ops.QueryContext;
    +import org.apache.drill.exec.planner.logical.DrillTable;
    +import org.apache.drill.exec.util.ImpersonationUtil;
    +import org.apache.hadoop.fs.FSDataInputStream;
    +import org.apache.hadoop.fs.FileSystem;
    +import org.apache.hadoop.fs.Path;
    +import org.joda.time.DateTime;
    +
    +/**
    + * Wraps the stats table info including schema and tableName. Also materializes stats
from storage
    + * and keeps them in memory.
    + */
    +public class DrillStatsTable {
    +  private static final org.slf4j.Logger logger = org.slf4j.LoggerFactory.getLogger(DrillStatsTable.class);
    +  private final FileSystem fs;
    +  private final Path tablePath;
    +
    +  /**
    +   * List of columns in stats table.
    +   */
    +  public static final String COL_COLUMN = "column";
    +  public static final String COL_COMPUTED = "computed";
    +  public static final String COL_STATCOUNT = "statcount";
    +  public static final String COL_NDV = "ndv";
    +
    +  private final String schemaName;
    +  private final String tableName;
    +
    +  private final Map<String, Long> ndv = Maps.newHashMap();
    +  private double rowCount = -1;
    +
    +  private boolean materialized = false;
    +
    +  private TableStatistics statistics = null;
    +
    +  public DrillStatsTable(String schemaName, String tableName, Path tablePath, FileSystem
fs) {
    +    this.schemaName = schemaName;
    +    this.tableName = tableName;
    +    this.tablePath = tablePath;
    +    this.fs = ImpersonationUtil.createFileSystem(ImpersonationUtil.getProcessUserName(),
fs.getConf());
    +  }
    +
    +  public String getSchemaName() {
    +    return schemaName;
    +  }
    +
    +  public String getTableName() {
    +    return tableName;
    +  }
    +  /**
    +   * Get number of distinct values of given column. If stats are not present for the
given column,
    +   * a null is returned.
    +   *
    +   * Note: returned data may not be accurate. Accuracy depends on whether the table data
has changed after the
    +   * stats are computed.
    +   *
    +   * @param col
    +   * @return
    +   */
    +  public Double getNdv(String col) {
    +    // Stats might not have materialized because of errors.
    +    if (!materialized) {
    +      return null;
    +    }
    +    final String upperCol = col.toUpperCase();
    +    final Long ndvCol = ndv.get(upperCol);
    +    // Ndv estimation techniques like HLL may over-estimate, hence cap it at rowCount
    +    if (ndvCol != null) {
    +      return Math.min(ndvCol, rowCount);
    --- End diff --
    
    The resulting value, while seemingly useful, may have severe problems. Today we estimate
p(a = value) at 15%. This is big data, many columns will have a large NDV. Thus, p(a = value)
= 1/NDV which may be a very small number.
    
    When data is uniformly distributed, this may be fine. But, if data is skewed (mostly value
"A", with a sprinkling of "B" through "Z"), the estimate will be far off. We may end up thinking
that any equality filter greatly reduces row counts when it does not actually do so. Feed
the result into the build size of a hash join and "bad things happen."
    
    Should we be more conservative? Set some minimum value? Take a risk-based approach to
deciding which side of hash join to be the build side?


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