November 2019 – Large-Scale Data Engineering in Cloud

Memory, Presto

Presto – Troubleshooting Query Exceeded Per-Node Total Memory Limit – resource_overcommit, query.max-total-memory-per-node, Reserved Pool, Disk Spill

November 28, 2019

I had a SQL query that failed on one of the Presto 0.208 clusters with the “Query exceeded per-node total memory” (com.facebook.presto.ExceededMemoryLimitException) error. How can you solve this problem? I will consider a few possible solutions, but firstly let’s review the memory allocation in Presto.

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dmtolpeko
Hive, JVM, Tez

Apache Hive on Tez – Quick On The Fly Profiling of Long Running Tasks Using Jstack Probes and Flame Graphs

November 20, 2019

I was asked to diagnose and tune a long and complex ad-hoc Hive query that spent more than 4 hours on the reduce stage. The fetch from the map tasks and the merge phase completed fairly quickly (within 10 minutes) and the reducers spent most of their time iterating the input rows and performing the aggregations defined by the query – MIN, SUM, COUNT and PERCENTILE_APPROX and others on the specific columns.

After the merge phase a Tez reducer does not output many log records to help you diagnose the performance issues and find the bottlenecks. In this article I will describe how you can profile an already running Tez task without restarting the job.

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dmtolpeko
Hive, Tez

Apache Hive – Monitoring Progress of Long-Running Reducers – hive.log.every.n.records Option

November 16, 2019
Reducer aggregates rows within input groups (i.e. rows having the same grouping key) typically producing one output row per group. For example, the following query returns ~200 rows even if the source events table has billions of rows:
```
SELECT country, count(*) 
FROM events 
GROUP BY country;
```
The problem is that despite the small number of output rows the aggregation still can be a very long process taking many hours.
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dmtolpeko
Hive, Pig, Tez

Apache Hive/Pig on Tez – Long Running Tasks and Their Failed Attempts – Analyzing Performance and Finding Bottlenecks (Insufficient Parallelism) using Application Master Logs

November 1, 2019
Apache Tez is the main distributed execution engine for Apache Hive and Apache Pig jobs.

Tez represents a data flow as DAGs (Directed acyclic graph) that consists of a set of vertices connected by edges. Vertices represent data transformations while edges represent movement of data between vertices.

For example, the following Hive SQL query:
```
SELECT u.name, sum_items 
FROM
 (
   SELECT user_id, SUM(items) sum_items
   FROM sales
   GROUP BY user_id
 ) s
 JOIN
   users u
 ON s.user_id = u.id
```
and its corresponding Apache Pig script:
```
sales = LOAD 'sales' USING org.apache.hive.hcatalog.pig.HCatLoader();
users = LOAD 'users' USING org.apache.hive.hcatalog.pig.HCatLoader();

sales_agg = FOREACH (GROUP sales BY user_id)
  GENERATE 
    group.user_id as user_id,
    SUM(sales.items) as sum_items;
    
data = JOIN sales_agg BY user_id, users BY id;
```
Can be represented as the following DAG in Tez:

In my case the job ran almost 5 hours:

Why did it take so long to run the job? Is there any way to improve its performance?
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dmtolpeko