The ____ mechanism in HBase helps in balancing the load across the cluster.
- Compaction
- Distribution
- Replication
- Sharding
The compaction mechanism in HBase helps in balancing the load across the cluster. It involves merging smaller HFiles into larger ones, optimizing storage and improving performance by reducing file fragmentation.
What is the significance of Apache Tez in optimizing Hadoop's data processing capabilities?
- Data Flow Optimization
- Query Optimization
- Resource Management
- Task Scheduling
Apache Tez is significant in optimizing Hadoop's data processing capabilities by introducing a more flexible and efficient data flow model. It enables the optimization of the execution plan, allowing tasks to be executed in a directed acyclic graph (DAG) fashion, improving overall performance and resource utilization.
____ in Apache Spark is used for processing large scale streaming data in real-time.
- Spark Batch
- Spark Streaming
- Spark Structured Streaming
- SparkML
Spark Structured Streaming in Apache Spark is used for processing large-scale streaming data in real-time. It provides a high-level API for stream processing with the same underlying engine as batch processing, offering ease of use and fault-tolerance.
The ____ component in Hadoop's security architecture is responsible for storing and managing secret keys.
- Authentication Server
- Credential Store
- Key Management Service
- Security Broker
The Credential Store component in Hadoop's security architecture is responsible for storing and managing secret keys. It helps secure sensitive information such as credentials and encryption keys, enhancing the overall security of the Hadoop ecosystem.
What is a recommended practice for optimizing MapReduce job performance in Hadoop?
- Data Replication
- Input Compression
- Output Serialization
- Task Parallelism
Optimizing MapReduce job performance involves considering the format of input data. Using input compression, such as Hadoop's default compression codecs, can reduce the amount of data transferred between nodes, improving job efficiency.
Advanced use of Hadoop Streaming API involves the implementation of ____ for efficient data sorting and aggregation.
- Flink
- MapReduce
- Spark
- Tez
Advanced use of Hadoop Streaming API involves the implementation of MapReduce for efficient data sorting and aggregation. MapReduce is a key processing model in Hadoop, and integrating it with Streaming API allows for complex data processing tasks, including sorting and aggregation, in a distributed fashion.
In a scenario of sudden performance degradation in a Hadoop cluster, what should be the primary focus of investigation?
- Disk I/O
- Memory Usage
- Network Latency
- Task Execution Logs
In a sudden performance degradation scenario, the primary focus should be on memory usage. High memory consumption can lead to performance issues as it affects task execution and overall cluster efficiency. Analyzing memory usage can help identify resource bottlenecks and optimize performance.
For advanced Hadoop development, ____ is crucial for integrating custom processing logic.
- Apache Hive
- Apache Pig
- Apache Spark
- HBase
For advanced Hadoop development, Apache Spark is crucial for integrating custom processing logic. Spark provides a powerful and flexible platform for big data processing, supporting advanced analytics, machine learning, and custom processing through its rich set of APIs.
The ____ compression codec in Hadoop is known for its high compression ratio and decompression speed.
- Bzip2
- Gzip
- LZO
- Snappy
The Snappy compression codec in Hadoop is renowned for its high compression ratio and fast decompression speed. It is particularly suitable for scenarios where low latency is crucial, making it a popular choice for big data processing.
In HBase, how are large tables divided and distributed across the cluster?
- Columnar Partitioning
- Hash Partitioning
- Range Partitioning
- Row-Key Partitioning
Large tables in HBase are divided and distributed across the cluster based on Row-Key Partitioning. Rows with similar Row-Key values are grouped together, and the distribution is determined by the Row-Key, facilitating efficient data retrieval and parallel processing.