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Apache Spark provides several environments and options that you can use to configure and manage your data processing tasks. Here are some of the key environments and options available in Spark:
- Spark Configuration Options:
- SparkConf: This is used to configure various Spark parameters as key-value pairs within your application.
- Environment Variables: You can set certain environment variables that Spark uses to determine its behavior. Examples include SPARK_HOME, which defines the installation directory of Spark, and SPARK_LOCAL_IP, used to set the IP address Spark binds to on the local machine.
- Runtime Environments:
- Local Mode: Runs Spark on your local machine, which is useful for development and testing. You can specify this mode using "local" or "local[*]" as the master URL, which will use one thread or as many threads as logical cores on your machine, respectively.
Running Spark on your local machine is the most straightforward way to start with Spark. It's suitable for development, small-scale data processing, and testing Spark applications. When to Use: - Development and Testing: You can employ a local machine to develop and test Spark applications before you deploy them to a larger cluster.
- Small Data Sets: You can use a local machine for small data sets that fit in your computer's memory.
- Learning and Prototyping: Local machines are ideal for learning Spark or prototyping Spark applications.
- Adjusting settings on a per-machine basis: For adjusting settings on a per-machine basis, the most suitable method is typically through the use of environment variables.
- Standalone Cluster Mode: Uses Spark’s own cluster manager to handle resource allocation. It's a simple cluster mode ideal for setting up a private cluster.
- On-Premises Cluster:
Deploying Spark on an on-premises cluster involves setting up a cluster of physical servers within your own data center. This helps you gain more control over hardware and network configurations. When to Use: - Data Security and Compliance: You can use the on-premises cluster approach when on-premises data processing becomes mandatory according to the data security and compliance requirements.
- Resource Control: With the on-premises cluster approach, you can control over hardware resources completely, making it suitable for specific hardware requirements.
- Long-term Stability: You can use the on-premises cluster approach if your organization is committed to on-premises infrastructure.
- Mesos: Integrates with Apache Mesos to leverage its advanced resource scheduling capabilities.
- YARN: Runs Spark on top of Hadoop YARN, allowing for resource management and scheduling in a Hadoop ecosystem.
- Kubernetes: Spark can run on Kubernetes, providing efficient container management and orchestration.
- Cloud: Deploying Apache Spark on the cloud provides you with scalable and flexible solutions for data processing. In the cloud, you can manage your own Spark cluster or leverage managed services offered by public cloud providers.
- IBM Cloud: IBM Cloud offers Spark support through IBM Cloud Pak for Data. This provides a unified data and AI platform with Spark capabilities.
When to Use: - IBM Ecosystem: IBM Cloud is a seamless choice if your organization uses IBM technologies and services.
- Data and AI Integration: IBM Cloud can be utilized by organizations wanting to integrate Spark with AI and machine learning workflows.
- Hybrid Cloud: IBM Cloud is suitable for hybrid cloud deployments, helping you to connect on-premises and cloud-based resources.
- Azure HDInsight: Azure HDInsight is a cloud-based big data platform by Microsoft that supports Spark and other big data tools. It offers a managed environment and allows integration into Azure services.
When to Use: - Microsoft Ecosystem: If your organization relies on Microsoft technologies, HDInsight provides you with a natural fit for Spark integration.
- Managed Services: Azure HDInsight plays a part when you want a fully managed Spark cluster without worrying about infrastructure management.
- Hybrid Deployments: Azure HDInsight is ideal for hybrid deployments where some data resides on-premises and some in Azure.
- AWS EMR (Elastic MapReduce): Amazon EMR is a cloud-based big data platform that makes it easy for Spark to run on AWS. EMR offers scalability, easy management, and integration with other AWS services.
When to Use: - Scalability: EMR allows you to process large data sets and scale resources up or down based on demand.
- AWS Integration: If your data ecosystem is already on AWS, EMR can integrate with other AWS services seamlessly.
- Cost Efficient: EMR allows you to pay only for the resources you use, making it cost-effective for variable workloads.
- Databricks: Databricks is a unified analytics platform that offers you a fully managed Spark environment. It simplifies Spark deployment, management, and collaboration among data teams.
When to Use: - Collaboration: When multiple data teams need to work together on Spark projects, Databricks provides you with collaboration features.
- Managed Environment: Databricks takes care of infrastructure, making it easier for you to focus on data processing and analysis.
- Advanced Analytics: Databricks is suitable for advanced analytics and machine learning projects due to integrated libraries and notebooks.
- Development Environments:
- Interactive Environments: Spark supports interactive environments through the Spark Shell for Scala or Python (PySpark), allowing for interactive data analysis and exploration.
- Notebook Environments: Tools like Jupyter notebooks are commonly used with Spark, especially PySpark, to create and share documents containing live code, equations, visualizations, and narrative text.
- Data Processing Options:
- RDDs, DataFrames, and Datasets: These are the core data structures in Spark. RDDs offer low-level functionality and fine-grained control, while DataFrames and Datasets provide higher-level abstractions with optimizations through Spark SQL’s Catalyst optimizer.
- Streaming: Spark Streaming allows for processing real-time data streams. DStreams are the basic abstraction in Spark Streaming.
- Graph Processing: GraphX is Spark's API for graph computation, enabling the processing of graph data at scale.
- Machine Learning: MLlib is Spark’s scalable machine learning library which simplifies machine learning pipelines on big data.
- Configuration and Tuning Options:
- Dynamic Allocation: Enables Spark to dynamically scale the number of executors used for an application according to the workload.
- Caching and Persistence: Users can persist intermediate datasets in memory or on disk that are used across multiple stages in Spark applications.
- Advanced Tuning: Parameters like spark.executor.memory, spark.core.max, etc., can be tuned for optimizing performance.
Table 3316. Cheatsheet of Development and Runtime Environment Options.
| Package or method |
Code example |
Description |
| SparkSession.builder.getOrCreate() |
from pyspark.sql import SparkSession
Create a SparkSession or get an existing one:
spark = SparkSession.builder.appName("myApp").getOrCreate()
Use the spark object to work with Spark |
Gets an existing SparkSession or, if there is no existing one, creates a new one based on the options set in this builder. |
| cd |
Basic syntax of cd command:
cd [options]... [directory]
Example 1: Change directory location to folder1:
cd /usr/local/folder1
Example 2: Get back to the previous working directory
cd -
Example 3: Move up one level from the present working directory tree
cd .. |
Used to move efficiently from the existing working directory to different directories on your system. |
| docker-compose |
Example (docker-compose.yml)
version: '3'
services:
web:
image: nginx:latest
ports:
- "80:80"
db:
image: postgres:latest |
Tool for defining and running multicontainer Docker applications. It uses the YAML file to configure the services and enables us to create and start all the services from just one configuration file. |
| git clone |
git clone REPOSITORY_URL [DESTINATION_DIRECTORY] |
You can create a copy of a specific repository or branch within a repository. |
| import |
from pyspark.sql import SparkSession |
Used to make code from one module accessible in another. Python imports are crucial for a successful code structure. You may reuse code and keep your projects manageable by using imports effectively to increase productivity. |
| pip |
pip list |
To ensure that requests will function, the pip program searches for the package in the Python Package Index (PyPI), resolves any dependencies, and installs everything in your current Python environment. |
| pip install |
pip install package_name |
The pip install <package> command looks for the latest version of the package and installs it. |
| pip3 install |
pip3 install package_name |
pip3 is the official package manager and pip command for Python 3. It enables installing and managing third-party software packages with features and functionality not found in the Python standard library. Pip3 installs packages from PyPI (Python Package Index), but it won't resolve dependencies or help you solve dependency conflicts. |
| print() |
print("Hello, World!") |
Prints the specified message to the screen or other standard output device.
The message can be a string or any other object; the object will be converted into a string before being written to the screen. |
| python3 |
sudo apt-get update
sudo apt-get install python3
python3 –-version #to verify the python version |
Python 3 is a widely used programming language known for its readability and versatility. |
| sc.setloglevel() |
Import necessary modules:
from pyspark import SparkContext
Create a SparkContext:
sc = SparkContext("local", "LogLevelExample")
Set the log level to a desired level (e.g., INFO, ERROR):
sc.setLogLevel("INFO")
Now, any logging messages with a severity level equal to or higher than INFO will be displayed |
Using this method, you can change the log level to the desired level. Valid log levels include ALL, DEBUG, ERROR, FATAL, INFO, OFF, TRACE, and WARN. |
| setMaster() |
<pre>from pyspark import SparkContext </pre>
<p>Create a SparkContext with a local master:</p>
<pre>sc = SparkContext("local[*]", "MyApp")</pre></td> |
Denotes where to run your Spark application, local or cluster. When you run on a cluster, you need to specify the address of the Spark master or Driver URL for a distributed cluster. We usually assign a local[*] value to setMaster() in Spark while doing internal testing. |
| show() |
df.show() |
Spark DataFrame show() is used to display the contents of the DataFrame in a table row and column format. By default, it shows only twenty rows, and the column values are truncated at twenty characters. |
| source |
Assuming a Bash shell:
source myscript.sh
|
Used to execute a script file in the current shell environment, allowing you to modify the current shell environment in the same way you would if you had typed commands manually. |
| virtualenv |
Creating a virtual environment named "myenv":
virtualenv myenv
|
Primarily a command-line application. It modifies the environment variables in a shell to create an isolated Python environment, so you'll need to have a shell to run it. You can type in virtualenv (name of the application), followed by flags that control its behavior. |
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