GridDB vs Tile38

Overview

Tile38

Stacks17

Followers41

Votes0

GitHub Stars9.5K

Forks597

GridDB

Stacks3

Followers18

Votes0

GitHub Stars0

Forks0

GridDB vs Tile38: What are the differences?

Introduction

In this comparison, we will highlight the key differences between GridDB and Tile38.

1. Scaling: GridDB is designed to handle large-scale data and provide high scalability with its distributed architecture, allowing it to handle huge amounts of data across multiple nodes. On the other hand, Tile38 focuses more on providing real-time geolocation data and has a smaller scale in terms of storage capacity.

2. Data Model: GridDB follows a row-oriented data model, which is suitable for structured data with complex relationships. It provides support for ACID transactions and consistency across the dataset. Meanwhile, Tile38 utilizes a key-value store data model, mainly focused on geospatial data management and retrieval.

3. Query Language: GridDB provides a rich query language called TQL (Time-Series Query Language), which offers a flexible and powerful way to query the database using SQL-like syntax. Tile38, in contrast, uses a custom query language specifically designed for geospatial data management, making it easier to work with location-based queries.

4. Indexing: GridDB offers various indexing options, including B-tree and hash indexes, to optimize query performance for different types of data. On the other hand, Tile38 has built-in support for geospatial indexing, allowing efficient retrieval of data based on geographical coordinates.

5. Replication and Fault Tolerance: GridDB provides built-in replication and fault tolerance mechanisms, ensuring high availability and data durability. It supports both synchronous and asynchronous replication for data redundancy. In contrast, Tile38 does not have built-in replication capabilities and relies on external solutions for replication and fault tolerance.

6. Use Cases: GridDB is suitable for a wide range of use cases that require high scalability, ACID transactions, and complex data relationships, such as IoT data management, time-series data analysis, and real-time analytics. Tile38, on the other hand, is specifically designed for location-based applications, such as geofencing, fleet tracking, and real-time geospatial data processing.

In Summary, GridDB is a scalable and versatile database system suitable for various data-intensive use cases, while Tile38 is more specialized in geolocation data management and processing.

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Manual

Detailed Comparison

Tile38	GridDB
It is an open source (MIT licensed), in-memory geolocation data store, spatial index, and realtime geofence. It supports a variety of object types including lat/lon points, bounding boxes, XYZ tiles, Geohashes, and GeoJSON.	It is a highly scalable, in-memory NoSQL time series database optimized for IoT and Big Data. It has a KVS (Key-Value Store)-type data model that is suitable for sensor data stored in a timeseries. It is a database that can be easily scaled-out according to the number of sensors.
Spatial index with search methods such as Nearby, Within, and Intersects; Realtime geofencing through webhooks or pub/sub channels; Object types of lat/lon, bbox, Geohash, GeoJSON, QuadKey, and XYZ tile; Support for lots of Clients Libraries written in many different languages; Variety of protocols, including http (curl), websockets, telnet, and the Redis RESP; Server responses are RESP or JSON; Full command line interface; Leader / follower replication; In-memory database that persists on disk	IoT Data Model; Distributed; Horizontal Scalability;In-memory;Hybrid Cluster Management;Fast Ingest;Composite Indexes;Petabyte-Scale DB size;Time series functions;Geometry data support
Statistics
GitHub Stars 9.5K	GitHub Stars 0
GitHub Forks 597	GitHub Forks 0
Stacks 17	Stacks 3
Followers 41	Followers 18
Votes 0	Votes 0
Integrations
Erlang PHP C++ Clojure Swift Windows Node.js Linux Java Python	Python Ubuntu Node.js CentOS Fluentd openSUSE

What are some alternatives to Tile38, GridDB?

Redis

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

Hazelcast

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

Related Comparisons

GridDB vs Tile38: What are the differences?

Introduction

In this comparison, we will highlight the key differences between GridDB and Tile38.

1. Scaling: GridDB is designed to handle large-scale data and provide high scalability with its distributed architecture, allowing it to handle huge amounts of data across multiple nodes. On the other hand, Tile38 focuses more on providing real-time geolocation data and has a smaller scale in terms of storage capacity.

2. Data Model: GridDB follows a row-oriented data model, which is suitable for structured data with complex relationships. It provides support for ACID transactions and consistency across the dataset. Meanwhile, Tile38 utilizes a key-value store data model, mainly focused on geospatial data management and retrieval.

3. Query Language: GridDB provides a rich query language called TQL (Time-Series Query Language), which offers a flexible and powerful way to query the database using SQL-like syntax. Tile38, in contrast, uses a custom query language specifically designed for geospatial data management, making it easier to work with location-based queries.

4. Indexing: GridDB offers various indexing options, including B-tree and hash indexes, to optimize query performance for different types of data. On the other hand, Tile38 has built-in support for geospatial indexing, allowing efficient retrieval of data based on geographical coordinates.

5. Replication and Fault Tolerance: GridDB provides built-in replication and fault tolerance mechanisms, ensuring high availability and data durability. It supports both synchronous and asynchronous replication for data redundancy. In contrast, Tile38 does not have built-in replication capabilities and relies on external solutions for replication and fault tolerance.

6. Use Cases: GridDB is suitable for a wide range of use cases that require high scalability, ACID transactions, and complex data relationships, such as IoT data management, time-series data analysis, and real-time analytics. Tile38, on the other hand, is specifically designed for location-based applications, such as geofencing, fleet tracking, and real-time geospatial data processing.

In Summary, GridDB is a scalable and versatile database system suitable for various data-intensive use cases, while Tile38 is more specialized in geolocation data management and processing.

GridDB vs Tile38

Overview

GridDB vs Tile38: What are the differences?

Introduction