MarkLogic vs Scylla

Overview

MarkLogic

Stacks43

Followers71

Votes26

ScyllaDB

Stacks143

Followers197

Votes8

MarkLogic vs Scylla: What are the differences?

Data Model: MarkLogic uses a flexible document data model, allowing developers to structure and work with data in various formats, such as JSON, XML, and RDF. On the other hand, Scylla uses a column-family data model, which is more suited for high-speed data retrieval and storage in a distributed environment.
Query Language: MarkLogic utilizes its own query language called XQuery, which is specifically designed for querying XML data and provides expressive capabilities for complex queries. In contrast, Scylla uses CQL (Cassandra Query Language), which is similar to SQL and allows for easy interaction with the database through familiar query structures.
Consistency Model: MarkLogic offers ACID transactions with immediate consistency, ensuring data integrity and reliability during read and write operations. Scylla, being based on Apache Cassandra, provides eventual consistency by default, focusing on high availability and partition tolerance in distributed systems.
Indexing: MarkLogic uses a universal index to efficiently retrieve data regardless of the data format or structure, enabling quick searches and analytics. Scylla relies on secondary indexes, which need to be carefully managed and maintained for optimal query performance.
Replication and Sharding: MarkLogic supports multi-model data replication and automatic sharding, making it easier to distribute data across multiple nodes for scalability and fault tolerance. In comparison, Scylla emphasizes masterless architecture for seamless horizontal scalability and resilience in distributed environments.
Workload Types: MarkLogic is well-suited for complex transactional workloads that require real-time data processing and analysis, making it ideal for applications with stringent performance requirements. Conversely, Scylla excels in handling high-throughput and low-latency workloads, such as time-series data processing and IoT applications.

In Summary, MarkLogic and Scylla differ in their data models, query languages, consistency models, indexing strategies, replication/sharding mechanisms, and workload suitability in various application scenarios.

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Advice on MarkLogic, ScyllaDB

Tom

CEO at Gentlent

Jun 9, 2020

Decided

The Gentlent Tech Team made lots of updates within the past year. The biggest one being our database:

We decided to migrate our #PostgreSQL -based database systems to a custom implementation of #Cassandra . This allows us to integrate our product data perfectly in a system that just makes sense. High availability and scalability are supported out of the box.

387k views387k

Comments

Vinay

Head of Engineering

Sep 19, 2019

Needs advice

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.

174k views174k

Comments

Detailed Comparison

MarkLogic	ScyllaDB
MarkLogic is the only Enterprise NoSQL database, bringing all the features you need into one unified system: a document-centric, schema-agnostic, structure-aware, clustered, transactional, secure, database server with built-in search and a full suite of application services.	ScyllaDB is the database for data-intensive apps that require high performance and low latency. It enables teams to harness the ever-increasing computing power of modern infrastructures – eliminating barriers to scale as data grows.
Search and Query;ACID Transactions;High Availability and Disaster Recovery;Replication;Government-grade Security;Scalability and Elasticity;On-premise or Cloud Deployment;Hadoop for Storage and Compute;Semantics;Faster Time-to-Results	High availability; horizontal scalability; vertical scalability; Cassandra compatible; DynamoDB compatible; wide column; NoSQL; lightweight transactions; change data capture; workload prioritization; shard-per-core; IO scheduler; self-tuning
Statistics
Stacks 43	Stacks 143
Followers 71	Followers 197
Votes 26	Votes 8
Pros & Cons
Pros 5 RDF Triples 3 REST API 3 Marklogic is absolutely stable and very fast 3 JavaScript 3 Enterprise	Pros 2 Replication 1 Fewer nodes 1 High performance 1 Written in C++ 1 High availability
Integrations
No integrations available	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to MarkLogic, ScyllaDB?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

MarkLogic vs Scylla: What are the differences?

Data Model: MarkLogic uses a flexible document data model, allowing developers to structure and work with data in various formats, such as JSON, XML, and RDF. On the other hand, Scylla uses a column-family data model, which is more suited for high-speed data retrieval and storage in a distributed environment.
Query Language: MarkLogic utilizes its own query language called XQuery, which is specifically designed for querying XML data and provides expressive capabilities for complex queries. In contrast, Scylla uses CQL (Cassandra Query Language), which is similar to SQL and allows for easy interaction with the database through familiar query structures.
Consistency Model: MarkLogic offers ACID transactions with immediate consistency, ensuring data integrity and reliability during read and write operations. Scylla, being based on Apache Cassandra, provides eventual consistency by default, focusing on high availability and partition tolerance in distributed systems.
Indexing: MarkLogic uses a universal index to efficiently retrieve data regardless of the data format or structure, enabling quick searches and analytics. Scylla relies on secondary indexes, which need to be carefully managed and maintained for optimal query performance.
Replication and Sharding: MarkLogic supports multi-model data replication and automatic sharding, making it easier to distribute data across multiple nodes for scalability and fault tolerance. In comparison, Scylla emphasizes masterless architecture for seamless horizontal scalability and resilience in distributed environments.
Workload Types: MarkLogic is well-suited for complex transactional workloads that require real-time data processing and analysis, making it ideal for applications with stringent performance requirements. Conversely, Scylla excels in handling high-throughput and low-latency workloads, such as time-series data processing and IoT applications.

MarkLogic vs Scylla

Overview