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Apache Parquet vs Cassandra: What are the differences?
Key Differences between Apache Parquet and Cassandra
Apache Parquet and Cassandra are both widely used technologies in the data storage and processing domain. While they have some similarities, there are several key differences between them.
Data Storage Model: Apache Parquet is a columnar storage file format that is designed for efficient data processing and analytics. It stores data in a column-wise manner, which allows for fast and efficient querying. On the other hand, Cassandra is a distributed NoSQL database that is designed for high availability and scalability. It uses a partitioned row store model to store data across a cluster of machines.
Data Model: Apache Parquet does not have a built-in data model. It is a schema evolution-supporting format that can work with various data models, including relational and nested data structures. Cassandra, on the other hand, has a flexible and dynamic data model based on a key-value pair model. It allows for dynamic column addition and removal without affecting the existing data.
Data Consistency: Apache Parquet does not provide any built-in mechanisms for data consistency and does not support transactions. It is primarily used for batch processing and analytics workloads. On the other hand, Cassandra provides built-in mechanisms for data consistency and supports distributed transactions. It is designed for high write throughput and can handle real-time workloads.
Data Replication and Distribution: Apache Parquet does not have built-in replication and distribution mechanisms, as it is typically used with other distributed storage systems like Hadoop Distributed File System (HDFS) or cloud storage solutions. Cassandra, on the other hand, has built-in replication and distribution mechanisms, which allow it to provide high availability and fault tolerance.
Querying and Indexing: Apache Parquet is optimized for efficient analytical processing and supports predicate pushdown, which improves query performance by pushing filters down to the storage layer. It also supports dictionary encoding and compression techniques to further improve query performance. Cassandra, on the other hand, supports a query language called Cassandra Query Language (CQL) and provides secondary indexing capabilities. It is designed for real-time querying and provides low-latency read and write operations.
Data Consistency and Availability Guarantees: Apache Parquet does not provide any specific guarantees for data consistency or availability. It relies on the underlying storage system for durability and availability. Cassandra, on the other hand, provides tunable consistency and availability levels. It uses a distributed architecture with replication and automatic data repair mechanisms to ensure high data availability and fault tolerance.
In Summary, Apache Parquet is a columnar storage file format optimized for analytics workloads, while Cassandra is a distributed NoSQL database designed for high availability and scalability with support for real-time querying and transactions.
The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.
The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.
Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.
By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.
Pros of Apache Parquet
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
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Cons of Apache Parquet
Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
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