docs and actions update

This commit is contained in:
Jonathan Shook 2020-03-17 08:51:13 -05:00
parent 90dae2fdfb
commit 7ae66dca2b
2 changed files with 46 additions and 2 deletions

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@ -128,7 +128,7 @@ jobs:
asset_path: nb/target/nb.jar
asset_name: nb.jar
asset_content_type: application/octet-stream
upload_url: https://uploads.github.com/repos/nosqlbench/nosqlbench/releases/${LATEST_GH_RELEASE_ID}/assets?name=nb.jar
upload_url: https://uploads.github.com/repos/nosqlbench/nosqlbench/releases/${{ env.LATEST_GH_RELEASE_ID }}/assets?name=nb.jar
- name: upload binary
uses: actions/upload-release-asset@v1
env:
@ -137,7 +137,7 @@ jobs:
asset_path: nb/target/nb
asset_name: nb
asset_content_type: application/octet-stream
upload_url: https://uploads.github.com/repos/nosqlbench/nosqlbench/releases/${LATEST_GH_RELEASE_ID}/assets?name=nb
upload_url: https://uploads.github.com/repos/nosqlbench/nosqlbench/releases/${{ env.LATEST_GH_RELEASE_ID }}/assets?name=nb
docs:
needs: release

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@ -0,0 +1,44 @@
---
title: NoSQLBench Showcase
weight: 80
---
Since NoSQLBench is new on the scene in its current form, you may be wondering
why you would want to use it over any other tool. That is what this section is all
about.
First, a brief overview, vis-a-vis some tools you may have already used.
NoSQLBench can do everything that other testing tools can do, and more. It
achieves this by focusing on a scalable user experience in combination with a
modular internal architecture.
NoSQLBench is a workload construction and simulation tool for scalable systems
testing. That is an entirely different scope of endeavor than most other tools.
Here are some of the serious capabilities that are unique to NoSQLBench:
- Metrics are built-in
- Scenario Scripting
- Command Line Scripting
- Scripting Extensions
- Multi-Protocol Support
- Analysis Methods
- Deterministic Workloads
- Advanced Variate Sampling
- Attention to Performance
- Coordinated Omission is not special
- Synchronous and Asynchronous
- Flexible Sequencing
- Advanced Rate Limiting
- Blazing fast Procedural Data Generation
- Virtual Data Set Recipes with Lambdas
- Experimentation Friendly
- Industrial Strength
- Built-In Seed Data
- Not limited by a "DSL"
1. You can control your test data, down to the operation.
2. All operations are deterministic according to the cycle.
3. The core concepts are battle tested and refined
4. We serve quick workflows as well as advanced testing scenarios.