ACID properties

The ACID properties are a set of principles that guarantee reliable transactions in a database system. 
ACID stands for Atomicity, Consistency, Isolation, and Durability, and these properties apply to various data management systems like SQL databases, Hive, and PySpark. 
Here's how they are interpreted and handled in these systems:

ACID in PySpark
PySpark, the Python API for Apache Spark, works with distributed data and supports some ACID-like properties, 
but Spark itself does not natively support full ACID compliance. However, modern Delta Lake and Hudi introduce ACID transactions in Spark. 
Here's how Spark handles ACID properties:

Atomicity:
Definition: PySpark doesn’t inherently ensure atomicity across transformations and actions on RDDs/DataFrames. However, with Delta Lake, you can achieve atomicity.
Delta Lake: Delta Lake (which runs on top of Apache Spark) ensures that either all changes in a transaction are written to disk or none of them are.
Example: A MERGE or UPDATE operation in Delta Lake is atomic.
Consistency:
Definition: Data remains consistent in PySpark and adheres to the schema and rules defined at the start.
PySpark: PySpark allows transformations to be applied consistently, but schema enforcement isn't as strict as SQL databases. 
With Delta Lake, schema enforcement and schema evolution ensure consistency during write operations.
Isolation:
Definition: PySpark transformations are executed lazily and only materialized upon action (e.g., count(), show()). Delta Lake supports isolation.
Delta Lake: Delta Lake supports serializable isolation, ensuring that changes made in one transaction are not visible to other transactions until committed.
Durability:
Definition: Changes in a Spark job may not be durable unless committed to a persistent storage layer.
Delta Lake: Delta Lake writes data to a durable storage medium (like HDFS, S3, or local file systems) and guarantees that the data is durable even in case of failures.
Transactional Support:
PySpark + Delta Lake/Hudi: Both Delta Lake and Apache Hudi offer ACID properties to handle transactions in a distributed environment, 
allowing you to manage data at scale with consistency and atomicity guarantees.
Delta Lake Example:
# Example of Delta Lake Transactional Update
from delta.tables import DeltaTable

# Create a Delta Table
delta_table = DeltaTable.forPath(spark, "/path/to/delta-table")

# Perform an update operation atomically
delta_table.update(
    condition = "id == 1",
    set = { "value": "'updated_value'" }
)

Property	        SQL (Traditional Databases)	                 Hive (With ACID Tables)	                    PySpark (With Delta Lake/Hudi)
Atomicity        	Fully supported via transactions	           Supported for transactional tables	          Supported with Delta Lake/Hudi
Consistency	      Ensured via constraints and integrity	       Ensured via ORC tables & checks	            Ensured with Delta Lake schema enforcement
Isolation	        Supported with isolation levels	             Supported via locks and partitions	          Serializable isolation with Delta Lake/Hudi
Durability	      Guaranteed with write-ahead logging	         Guaranteed via HDFS and logs	                Durable with storage layers like HDFS, S3


Summary:
SQL databases (like MySQL, PostgreSQL) natively support all ACID properties for reliable transaction management.
Hive has limited support for ACID transactions, focusing on insert/update/delete operations with transactional tables (usually ORC-formatted).
PySpark itself doesn’t fully implement ACID properties, but with Delta Lake or Apache Hudi, you can get ACID-compliant transactions in a distributed environment.