tape
A lightning fast, transactional, file-based FIFO for Android and Java. (by square)
Big Queue
A big, fast and persistent queue based on memory mapped file. (by bulldog2011)
| tape | Big Queue | |
|---|---|---|
| - | - | |
| 2,465 | 563 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| over 2 years ago | almost 3 years ago | |
| Java | Java | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tape
Posts with mentions or reviews of tape.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning tape yet.
Tracking mentions began in Dec 2020.
Big Queue
Posts with mentions or reviews of Big Queue.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning Big Queue yet.
Tracking mentions began in Dec 2020.
What are some alternatives?
When comparing tape and Big Queue you can also consider the following projects:
Wire - gRPC and protocol buffers for Android, Kotlin, Swift and Java.
Persistent Collection - A Persistent Java Collections Library
Apache Thrift - Apache Thrift
Apache Orc - Apache ORC - the smallest, fastest columnar storage for Hadoop workloads