What are the issues faced in spark?
Table of Contents
What are the issues faced in spark?
Ten Spark Challenges
| Job-Level Challenges | Cluster-Level Challenges |
|---|---|
| 2. Memory allocation | 7. Observability |
| 3. Data skew / small files | 8. Data partitioning vs. SQL queries / inefficiency |
| 4. Pipeline optimization | 9. Use of auto-scaling |
| 5. Finding out whether a job is optimized | 10. Troubleshooting |
What are the pros and cons of spark streaming?
Pros and Cons of Apache Spark
| Apache Spark | Advantages | Disadvantages |
|---|---|---|
| Dynamic in Nature | Small Files Issue | |
| Multilingual | Window Criteria | |
| Apache Spark is powerful | Doesn’t suit for a multi-user environment | |
| Increased access to Big data | – |
When should you not use spark?
When Not to Use Spark
- Ingesting data in a publish-subscribe model: In those cases, you have multiple sources and multiple destinations moving millions of data in a short time.
- Low computing capacity: The default processing on Apache Spark is in the cluster memory.
What is the main advantage of Apache spark?
Speed. Engineered from the bottom-up for performance, Spark can be 100x faster than Hadoop for large scale data processing by exploiting in memory computing and other optimizations. Spark is also fast when data is stored on disk, and currently holds the world record for large-scale on-disk sorting.
Why do Spark jobs fail?
In Spark, stage failures happen when there’s a problem with processing a Spark task. These failures can be caused by hardware issues, incorrect Spark configurations, or code problems. When a stage failure occurs, the Spark driver logs report an exception like this: org.
What are the advantages and limitations of Apache Spark when compared with MapReduce?
Apache Spark is well-known for its speed. It runs 100 times faster in-memory and 10 times faster on disk than Hadoop MapReduce. The reason is that Apache Spark processes data in-memory (RAM), while Hadoop MapReduce has to persist data back to the disk after every Map or Reduce action.
What is Apache Spark ecosystem?
The Apache Spark ecosystem is an open-source distributed cluster-computing framework. Spark is a data processing engine developed to provide faster and easier analytics than Hadoop MapReduce. It has quickly become the largest open source community in big data, with over 1000 contributors from 250+ organizations.