Apache Parquet

Website

parquet.apache.org

$ Details

Categories

#Databases #Big Data #NoSQL Databases #Key-Value Database

KeyDB

Website

docs.keydb.dev

$ Details

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Categories

#Key-Value Database #Databases #NoSQL Databases #Relational Databases

Apache Parquet features and specs

• Columnar Storage
  Apache Parquet uses columnar storage, which allows for efficient retrieval of only the data you need, reducing I/O and improving query performance on large datasets.
• Compression
  Parquet files support efficient compression and encoding schemes, resulting in significant storage savings and less data to transfer over the network.
• Compatibility
  It is compatible with the Hadoop ecosystem, including tools like Apache Spark, Hive, and Impala, making it versatile for big data processing.
• Schema Evolution
  Parquet supports schema evolution, allowing changes to the schema without breaking existing data, which helps in maintaining long-lived data pipelines.
• Efficient Read Performance for Aggregations
  Due to its columnar layout, Parquet is highly efficient for processing queries that aggregate data across columns, such as SUM and AVERAGE.

Possible disadvantages of Apache Parquet

• Write Performance
  Writing data to Parquet can be slower compared to row-based formats, particularly for small inserts or updates, due to the overhead of encoding and compression.
• Complexity in File Management
  Managing and partitioning Parquet files to optimize performance can become complex, particularly as datasets grow in size and complexity.
• Not Ideal for All Workloads
  Workloads that require frequent row-level updates or involve small queries might be less efficient with Parquet due to its columnar nature.
• Learning Curve
  The need to understand the nuances of columnar storage, encoding, and compression can pose a learning curve for teams new to Parquet.

KeyDB features and specs

• High Performance
  KeyDB offers superior performance over Redis by allowing multi-threading, which utilizes multiple CPU cores efficiently, leading to significant improvements in throughput and latency.
• Redis Compatibility
  KeyDB is fully compatible with Redis, meaning users can easily switch between Redis and KeyDB without needing to change their existing code or data structures.
• Active Replication
  It supports multi-primary (active-active) replication, enabling all replicas to accept writes without worrying about conflicts, which increases availability and resilience.
• Built-in TLS
  KeyDB includes built-in TLS support which enhances security by allowing data encryption in transit, a feature that requires third-party solutions in some Redis setups.
• Persistence Options
  KeyDB supports both RDB snapshotting and AOF logging, offering flexible persistence strategies to balance between performance and durability.

Possible disadvantages of KeyDB

• Community Size
  KeyDB, while gaining popularity, has a smaller community compared to Redis, which can lead to less community support and fewer third-party tools or extensions.
• Maturity
  As a relatively newer project compared to Redis, KeyDB may lack the same level of proven stability and maturity, making it a potentially riskier choice for critical applications.
• Documentation and Resources
  While KeyDB has extensive documentation, it might not be as comprehensive or complete as Redis, potentially leading to longer project integration times.
• Potential Compatibility Issues
  Although KeyDB is compatible with Redis, advanced Redis features or unusual configurations might face compatibility issues during migration.
• Less Architectural Simplicity
  The added complexity of multi-threading and active-active replication modes can increase the operational overhead compared to Redis's simpler single-threaded, master-slave architecture.

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