hello everyone,
Spark Concepts A to Z!!
A - Adaptive Query Execution (AQE):
Optimizes query plans dynamically based on runtime statistics for improved performance and resource utilization.
B - Broadcast Join:
Sends small datasets to all nodes for local joins, reducing shuffle overhead and enhancing performance.
C - Coalesce:
Reduces partitions in DataFrames/RDDs, optimizing performance by minimizing partition management overhead.
D - DataFrame:
Distributed collection of data organized into named columns, akin to a relational table for high-level data manipulation.
E - Executor:
Distributed agent executing tasks on worker nodes, managing data storage and caching.
F - Fault Tolerance:
Spark's ability to recover from node failures using lineage information to recompute lost data partitions.
G - GroupBy:
Aggregates data based on keys, enabling operations like sum, average, and count on grouped data.
H - HiveContext:
Supports Hive commands, UDFs, and interacts with Hive metastore within Spark SQL.
I - In-Memory Computing:
Stores data in RAM for faster iterative algorithms and processing tasks.
J - Join:
Combines rows from multiple DataFrames/RDDs based on related columns for complex data transformations.
K - Kryo Serialization:
Efficient serialization library optimizing network and disk I/O performance.
L - Lineage:
Tracks transformations on RDDs/DataFrames, ensuring fault tolerance by recomputing lost data.
M - Map:
Applies a function to each element in RDDs/DataFrames, creating new transformed datasets.
N - Narrow Dependency:
Optimizes data processing by ensuring each child partition depends on a single parent partition, reducing shuffling.
O - Optimization:
Improves query performance through techniques like predicate pushdown and cost-based optimization in Spark.
P - Partition:
Logical division of data in RDDs/DataFrames enabling parallel processing across Spark nodes.
Q - Query Execution Plan:
Outlines steps Spark takes to execute queries, from logical to physical and optimized plans.
R - Resilient Distributed Dataset (RDD):
Immutable, distributed collection of objects in Spark.
S - Spark SQL:
Module for structured data processing in Spark.
T - Transformations:
Operations creating new RDDs/DataFrames from existing ones, like map, filter, and reduceByKey.
U - Union:
Merges RDDs/DataFrames into a single dataset, combining their elements.
V - Vectorized Query Execution:
Processes multiple rows together using CPU caches and SIMD instructions for enhanced query performance.
W - Wide Dependency:
Occurs when child partitions depend on multiple parent partitions, often requiring shuffling in Spark processing.
X - XML Data Source:
Allows Spark to read and write XML files, supporting parsing and querying of XML data.
Y - Yarn:
Cluster manager to schedule and execute tasks across distributed nodes.
Z - Z-Ordering:
Optimizes data storage and retrieval by ordering rows based on column values, enhancing query performance.
