AWS – Data Engineering and Analytics in Cloud

Amazon, AWS, Hive, I/O, Parquet, S3, Spark

Spark – Slow Load Into Partitioned Hive Table on S3 – Direct Writes, Output Committer Algorithms

December 30, 2019

I have a Spark job that transforms incoming data from compressed text files into Parquet format and loads them into a daily partition of a Hive table. This is a typical job in a data lake, it is quite simple but in my case it was very slow.

Initially it took about 4 hours to convert ~2,100 input .gz files (~1.9 TB of data) into Parquet, while the actual Spark job took just 38 minutes to run and the remaining time was spent on loading data into a Hive partition.

Let’s see what is the reason of such behavior and how we can improve the performance.

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dmtolpeko
Amazon, AWS, RDS

Sqoop Import from Amazon RDS Read Replica – ERROR: Canceling Statement due to Conflict with Recovery

October 18, 2019
Apache Sqoop is widely used to import data from relational databases into cloud. One of our databases uses Amazon RDS for PostgreSQL to store sales data and its Sqoop import periodically failed with the following error:
```
Error: java.io.IOException: SQLException in nextKeyValue
Caused by: org.postgresql.util.PSQLException: ERROR: canceling statement due to conflict with recovery
  Detail: User query might have needed to see row versions that must be removed.
```
In this article I will describe a solution that helped resolve the problem in our specific case.
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dmtolpeko
AWS, EMR, Hadoop, YARN

Amazon EMR – Downscaling and Ghost (Impaired) Nodes

August 27, 2019

I already wrote about recovering the ghost nodes in Amazon EMR clusters, but in this article I am going to extend this information for EMR clusters configured for auto-scaling.

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dmtolpeko
AWS, EMR, Hadoop, YARN

Amazon EMR – Recovering Ghost Nodes

May 23, 2019

In a Hadoop cluster besides Active nodes you may also have Unhealthy, Lost and Decommissioned nodes. Unhealthy nodes are running but just excluded from scheduling the tasks because they, for example, do not have enough disk space. Lost nodes are nodes that are not reachable anymore. The decommissioned nodes are nodes that successfully terminated and left the cluster.

But all these nodes are known to the Hadoop YARN cluster and you can see their details such as IP addresses, last health updates and so on.

At the same time there can be also Ghost nodes i.e. nodes that are running by Amazon EMR services but Hadoop itself does not know anything about their existence. Let’s see how you can find them.

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dmtolpeko
AWS, EMR, Hadoop, YARN

Amazon EMR – Recovering Unhealthy Nodes with EMR Services Down

May 23, 2019

Usually Hadoop is able to automatically recover cluster nodes from Unhealthy state by cleaning log and temporary directories. But sometimes nodes stay unhealthy for a long time and manual intervention is necessary to bring them back.

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dmtolpeko
AWS, EMR, Hadoop, YARN

Amazon EMR – Monitoring Auto-Scaling using Instance Controller Logs

May 20, 2019

Amazon EMR allows you to define scale-out and scale-in rules to automatically add and remove instances based on the metrics you specify.

In this article I am going to explore the instance controller logs that can be very useful in monitoring the auto-scaling. The logs are located in /emr/instance-controller/log/ directory on the EMR master node.

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dmtolpeko
AWS, EMR, Hadoop, YARN

Hadoop YARN – Collecting Utilization Metrics from Multiple Clusters

May 15, 2019

When you run many Hadoop clusters it is useful to automatically collect metrics from all clusters in a single place (Hive table i.e.).

This allows you to perform any advanced and custom analysis of your clusters workload and not be limited to the features provided by Hadoop Administration UI tools that often offer only per cluster view so it is hard to see the whole picture of your data platform.

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dmtolpeko
Amazon, AWS, EMR, YARN

YARN Resource Manager Silent Restarts – Java Heap Space Error – Amazon EMR

January 4, 2019

When you run a job in Hadoop you can notice the following error: Application with id 'application_1545962730597_2614' doesn't exist in RM. And later looking at the YARN Resource Manager UI at http://<RM_IP_Address>:8088/cluster/apps you can see low Application ID numbers:

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dmtolpeko
Amazon, AWS, EMR, Hive, I/O, S3

S3 Writes When Inserting Data into a Hive Table in Amazon EMR

December 4, 2018
Often in an ETL process we move data from one source into another, typically doing some filtering, transformations and aggregations. Let’s consider which write operations are performed in S3.

Just to focus on S3 writes I am going to use a very simple SQL INSERT statement just moving data from one table into another without any transformations as follows:
```
INSERT OVERWRITE TABLE events PARTITION (event_dt = '2018-12-02', event_hour = '00')
SELECT
  record_id,
  event_timestamp,
  event_name,
  app_name,
  country,
  city,
  payload
FROM events_raw;
```
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dmtolpeko
Amazon, AWS, EMR, Hive, ORC, Tez

Tez Internals #2 – Number of Map Tasks for Large ORC Files with Small Stripes in Amazon EMR

November 12, 2018

Let’s see how Hive on Tez defines the number of map tasks when the input data is stored in large ORC files but having small stripes.

Note. All experiments below were executed on Amazon Hive 2.1.1. This article does not apply to Qubole running on Amazon AWS. Qubole has a different algorithm to define the number of map tasks for ORC files.

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dmtolpeko