Heroic vs Scylla

Overview

Heroic

Stacks5

Followers25

Votes0

GitHub Stars846

Forks106

ScyllaDB

Stacks143

Followers197

Votes8

Heroic vs Scylla: What are the differences?

Data Model - Heroic uses a hierarchical data model while Scylla utilizes a wide-column data model. The hierarchical data model in Heroic allows for organizing data in a tree-like structure, which can be beneficial for certain types of data querying and storage. On the other hand, Scylla's wide-column data model enables more flexibility and scalability in handling large amounts of data efficiently.
Consistency Level - Heroic supports tunable consistency levels for read and write operations, allowing users to adjust the trade-off between consistency and availability based on their specific requirements. In contrast, Scylla provides strong consistency by default, ensuring that all reads reflect the latest write, which can be advantageous for applications that prioritize data integrity over performance.
Query Language - Heroic utilizes a query language that is based on the MetricsQL syntax, making it easier for users familiar with metrics databases like Prometheus to write queries efficiently. In comparison, Scylla employs CQL (Cassandra Query Language), which is specifically designed for interacting with wide-column databases, offering a different set of capabilities and syntax tailored to that data model.
Secondary Indexes - Heroic supports secondary indexes, allowing users to query data based on non-primary key attributes efficiently. This feature can be valuable for scenarios where diverse query patterns are required. On the other hand, Scylla does not natively support secondary indexes, potentially limiting the flexibility of querying data by non-primary key attributes.
Partitioning Strategy - Heroic utilizes consistent hashing for partitioning data across nodes, providing a balanced distribution of data and efficient query processing. Conversely, Scylla uses partitioners to distribute data across the cluster based on hash values, offering a different approach to achieving optimal data distribution and query performance.
Ecosystem Integration - Heroic is tightly integrated with the wider ecosystem of tools and services in the observability space, allowing seamless integration with various data sources and visualization platforms. In contrast, Scylla is part of the Cassandra ecosystem, which may offer different integrations and compatibility with existing tools and frameworks.

In Summary, when considering Heroic versus Scylla, key differences include data model, consistency level, query language, secondary indexes, partitioning strategy, and ecosystem integration.

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Advice on Heroic, 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

Heroic	ScyllaDB
Heroic is Spotify's in-house time series database. It was built to address the challenges Spotify was facing with near real-time data collection and presentation at scale.	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.
heroic-core contains the com.spotify.heroic.HeroicCore class which is the central building block for setting up a Heroic instance.; heroic-elasticsearch-utils is a collection of utilities for interacting with Elasticsearch. This is separate since we have more than one backend that needs to talk with elasticsearch.; heroic-parser provides an Antlr4 implementation of com.spotify.heroic.grammar.QueryParser, which is used to parse the Heroic DSL.; heroic-shell contains com.spotify.heroic.HeroicShell, a shell capable of either running a standalone, or connecting to an existing Heroic instance for administration.	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
GitHub Stars 846	GitHub Stars -
GitHub Forks 106	GitHub Forks -
Stacks 5	Stacks 143
Followers 25	Followers 197
Votes 0	Votes 8
Pros & Cons
No community feedback yet	Pros 2 Replication 1 Written in C++ 1 High availability 1 High performance 1 Distributed
Integrations
No integrations available	KairosDB Wireshark JanusGraph Grafana Hackolade Prometheus Kubernetes Datadog Kafka Apache Spark

What are some alternatives to Heroic, 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

Heroic vs Scylla: What are the differences?

Data Model - Heroic uses a hierarchical data model while Scylla utilizes a wide-column data model. The hierarchical data model in Heroic allows for organizing data in a tree-like structure, which can be beneficial for certain types of data querying and storage. On the other hand, Scylla's wide-column data model enables more flexibility and scalability in handling large amounts of data efficiently.
Consistency Level - Heroic supports tunable consistency levels for read and write operations, allowing users to adjust the trade-off between consistency and availability based on their specific requirements. In contrast, Scylla provides strong consistency by default, ensuring that all reads reflect the latest write, which can be advantageous for applications that prioritize data integrity over performance.
Query Language - Heroic utilizes a query language that is based on the MetricsQL syntax, making it easier for users familiar with metrics databases like Prometheus to write queries efficiently. In comparison, Scylla employs CQL (Cassandra Query Language), which is specifically designed for interacting with wide-column databases, offering a different set of capabilities and syntax tailored to that data model.
Secondary Indexes - Heroic supports secondary indexes, allowing users to query data based on non-primary key attributes efficiently. This feature can be valuable for scenarios where diverse query patterns are required. On the other hand, Scylla does not natively support secondary indexes, potentially limiting the flexibility of querying data by non-primary key attributes.
Partitioning Strategy - Heroic utilizes consistent hashing for partitioning data across nodes, providing a balanced distribution of data and efficient query processing. Conversely, Scylla uses partitioners to distribute data across the cluster based on hash values, offering a different approach to achieving optimal data distribution and query performance.
Ecosystem Integration - Heroic is tightly integrated with the wider ecosystem of tools and services in the observability space, allowing seamless integration with various data sources and visualization platforms. In contrast, Scylla is part of the Cassandra ecosystem, which may offer different integrations and compatibility with existing tools and frameworks.

In Summary, when considering Heroic versus Scylla, key differences include data model, consistency level, query language, secondary indexes, partitioning strategy, and ecosystem integration.

Heroic vs Scylla

Overview