hive over tez event log

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Hive Over Tez Event Log

Introduction

Hive is a data warehousing infrastructure based on Apache Hadoop. It allows users to query and analyze large datasets stored in Hadoop Distributed File System (HDFS) using a SQL-like language called HiveQL. Hive provides a high-level interface to Hadoop, making it easier for users who are familiar with SQL to work with Hadoop.

Tez, on the other hand, is a general-purpose data processing framework built on top of Hadoop YARN. It provides a more efficient and scalable way to execute complex data processing tasks compared to the traditional MapReduce framework.

In this article, we will explore the event log generated by Hive when running queries over Tez. We will discuss the structure of the event log and how it can be useful for debugging and performance tuning Hive queries. We will also provide code examples to illustrate the concepts discussed.

Understanding the Hive Event Log

The Hive event log is a record of events that occur during the execution of a Hive query over Tez. It contains information about the tasks, stages, and other components involved in the query execution. The event log is stored in a file in a JSON format.

The event log can be enabled by setting the hive.tez.log.level configuration property to DEBUG. By default, the event log is disabled. Once enabled, the event log will be generated for each Hive query that runs over Tez.

The event log contains a hierarchy of events. Each event has a type, a timestamp, and additional attributes specific to that event type. The events are organized in a tree structure, where the root event represents the entire query and child events represent sub-tasks and sub-stages within the query.

Here is an example of a simple event log generated by Hive:

```json
{
  "events": [
    {
      "type": "QUERY_SUBMITTED",
      "timestamp": 1589366206308,
      "attributes": {
        "queryId": "query_20200101120000_0001",
        "query": "SELECT * FROM my_table"
      }
    },
    {
      "type": "QUERY_COMPLETED",
      "timestamp": 1589366206500
    }
  ]
}

This event log represents a query that selects all records from a table called my_table. It consists of two events: QUERY_SUBMITTED and QUERY_COMPLETED. The QUERY_SUBMITTED event provides information about the query, including its ID and the SQL statement. The QUERY_COMPLETED event indicates that the query has finished execution.

Analyzing the Event Log

The Hive event log can be useful for various purposes, including debugging and performance tuning Hive queries. By analyzing the event log, we can gain insights into the execution plan of the query, identify bottlenecks, and optimize the query for better performance.

One way to analyze the event log is to visualize it using a Gantt chart. A Gantt chart provides a graphical representation of the events and their durations. It allows us to see how different tasks and stages are scheduled and how they overlap with each other.

Here is an example of a Gantt chart generated from an event log:

```mermaid
gantt
    title Hive Query Execution

    section Stage 1
    Task 1 :done, 2020-05-13, 2d
    Task 2 :done, 2020-05-15, 1d

    section Stage 2
    Task 3 :done, 2020-05-14, 1d
    Task 4 :done, 2020-05-16, 2d

This Gantt chart represents a query with two stages. Each stage consists of one or more tasks. The chart shows the start and end dates of each task.

By analyzing the Gantt chart, we can identify any long-running tasks or stages that may be causing performance issues. We can also identify tasks or stages that can be parallelized to improve performance.

Conclusion

The Hive event log is a valuable tool for debugging and performance tuning Hive queries. It provides a detailed record of the events that occur during query execution, allowing us to analyze the execution plan and optimize the query for better performance.

In this article, we discussed the structure of the Hive event log and how it can be enabled for queries running over Tez. We also explored how to analyze the event log using a Gantt chart to visualize the execution plan and identify performance bottlenecks.

By leveraging the event log and analyzing it effectively, we can improve the performance of Hive queries and optimize the utilization of resources in a Hadoop cluster.