Mikeaddison93/instant-pdf. App Report Builder. The BaseSpace Report Builder allows you to hook into your output files and generate robust reports for the user. Reports can be created for any application you own in BaseSpace. If a report is not created for an application, the output files will be displayed in a file browser with a default report. Any app can use the Reports Builder tool, whether is it a Web, Desktop, or Native app. In the above example, the app has generated two AppResults.
The app has the result report enabled and the summary report disabled, so you can see that two reports are visible, one result report for each AppResult. How to Use the Reports Builder The Reports Builder tool can by used by any apps and will generate reports using HTML, CSS, JS, and the Dotliquid languages. Here is how a developer would first interact with the Reports Builder tool: Note: In order to start using the Reports Builder, the app must have an AppSession that is in the Complete status. Active Report Revisions Report Components The Summary Report.
Web Based Report Designer for Jasper Reports. Are you using Jasper Reports to generate reports in your web application? Would you like to easily add web based, ad hoc reporting that can integrate with your existing Jasper infrastructure? If so, here's how you can do it with jsreports. Get the jsreports trial package Download the jsreports trial package and extract its contents.
Basic setup First, add jsreports into your web application by loading the two necessary files, jsreports.min.js and jsreports.min.css. Next, you'll need to define at least one data source. jsreports can work with several types of data sources. Here's an example of declaring a data source client-side for jsreports to use: This array will tell jsreports that there is a data source on the server, with its schema available at "time-data-schema.json" and the data feed itself at the URL "time-data.json" (both static files in this example). Check the developer guide for more information on setting up jsreports in your application and defining data sources. Summary Stuck? JSON2HTML | Transform JSON to HTML. Dashboards for Graphite - Dashboard Dude. It’s no secret I like Graphite - it’s a great example of Open Source software which is just as good (actually, it’s even better) than similar closed-source, enterprise-grade (and much more expensive) software.
Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Introductions & Core Concepts Specific Techniques D3 v4 Blogs Books Courses D3.js in Motion (Video Course)Curran Kelleher, Manning Publications, September 2017D3 4.x: Mastering Data Visualization Nick Zhu & Matt Dionis, Packt.
Talks and Videos Meetups Research Papers D3: Data-Driven DocumentsMichael Bostock, Vadim Ogievetsky, Jeffrey HeerIEEE Trans. Mikeaddison93/mongo3 · GitHub. Mongodb-washington-dc-2013. Visualizing MongoDB in Objects in Concept and Practice. d3.js | is it just me. D3.js and MongoDB | Architecture and Planning. I have not been shy in my love of MongoDB. The honeymoon is not over. Now I want to graph and visualize my data from MongoDB.
I just started looking at D3 – I’m coming to the party a bit late – and it is perfect for this task. I have thrown together a super simple, absurd even, example using a DB I had already populated and some left over CherryPy code. Let me say that reusing code is a good idea – when I can find what I’m looking for. A Bar Chart, that should be in SVG, from MongoDB data in D3.js I have not put this on OpenShift yet, but may. The Python code prints out HTML that looks like this: Not much going on here, just a simple D3.js bar chart – not even done in SVG. I return all the HTML and you get the page displayed at the top of this post. Like this: Like Loading... Visualizing MongoDB Objects in Concept and Practice. Mongoviewer-server.
MongoDB + Node.js + Express + D3 Install API Server CLI MongoDB + Node.js + Express npm install -g mongoviewer-server Usage Start your MongoViewer API Server: mongoviewer-server By default it will serve on port 8080: See configuration options for customizing your API server. Example customized: mongoviewer-server --server:port 8081 --mongo:database testDb See the API documentation for more details.
Installation npm install bower install node index.js Configuration Uses nconf. See the default configuration file at default_config.json. Alternatively you can use additional command line arguments: --server:port 8080 - Default 8080. For example: node index.js --server:port 8081 --mongo:database testDb Version 1 /api/v1/ Find See db.collection.find. GET /api/v1/:collection/find Query Parameters: queryoptions FindOne See db.collection.findOne.
GET /api/v1/:collection/findOne Aggregate See db.collection.aggregate. GET /api/v1/:collection/aggregate pipelineoptions Example API Usage node load.js Response: How to visualize JSON documents from MongoDB | Open Source Big Data Analytics. Do you have a database full of JSON objects crying out for visual analysis? This post can help you take the first steps. Using free and Open Source libraries to visualize unstructured data sources was the topic of a recent presentation I gave at MongoDB Washington DC. The presentation covered the following topics: Visualizing Objects vs. The primary difference between objects (like MongoDB documents) and records is that objects can consist of dynamic and highly complex schemas versus the simple, static structures found in databases and delimited file formats like CSV files.
Most of the existing visualization tools used to create visualizations from relational databases and structured records aren’t designed to support building visualizations from unstructured, or dynamically structured, data sources. Extract your data from MongoDB for visualization Get to know JSON A quick introduction to two visualization tools Other visualization tools Learn More Visit the Resource Center.
D3.js tree diagram generated from external (JSON) data. NVD3. Mikeaddison93/dc.js. Graph-json. A JSON-format backed graph library with advanced identification algorithms. JSON Scheme (DirectedGraph): Documentation DirectedGraph You can use the DirectedGraph library in your project by either calling var DirectedGraph = require('graph-json').DirectedGraph; or var DirectedGraph = require('graph-json').DG; function DirectedGraph([struct]) Creates a directed graph based on the structure defined (a .json object matching the specification), or creates a graph with no edges and nodes if struct is not specified.
An example struct would look like the following (used in the remainder of the documentation) In order to create a graph, you'll want to parse your JSON object and pass it as a parameter. Var g = JSON.parse(fs.readFileSync('. Or, you can create a graph without specifying a JSON file: var t_graph = new DirectedGraph(); The graph above contains no nodes and no edges. DirectedGraph.prototype.edgesIn = function (node) Returns the number of edges entering a given node. Example: UndirectedGraph. Tag Aware Sharding — MongoDB Manual 2.6.6. MongoDB supports tagging a range of shard key values to associate that range with a shard or group of shards. Those shards receive all inserts within the tagged range. The balancer obeys tagged range associations, which enables the following deployment patterns: isolate a specific subset of data on a specific set of shards.ensure that the most relevant data reside on shards that are geographically closest to the application servers.
This document describes the behavior, operation, and use of tag aware sharding in MongoDB deployments. Behavior and Operations The balancer migrates chunks of documents in a sharded collections to the shards associated with a tag that has a shard key range with an upper bound greater than the chunk’s lower bound. During balancing rounds, if the balancer detects that any chunks violate configured tags, the balancer migrates chunks in tagged ranges to shards associated with those tags. Once configured, the balancer respects tag ranges during future balancing rounds. Sharding — MongoDB Manual 2.6.6. Sharding is the process of storing data records across multiple machines and is MongoDB’s approach to meeting the demands of data growth. As the size of the data increases, a single machine may not be sufficient to store the data nor provide an acceptable read and write throughput.
Sharding solves the problem with horizontal scaling. With sharding, you add more machines to support data growth and the demands of read and write operations. You can download this section in PDF form as Sharding and MongoDB. Sharding Introduction A high-level introduction to horizontal scaling, data partitioning, and sharded clusters in MongoDB. Sharding Concepts The core documentation of sharded cluster features, configuration, architecture and behavior. Sharded Cluster Components A sharded cluster consists of shards, config servers, and mongos instances. Sharded Cluster Architectures Outlines the requirements for sharded clusters, and provides examples of several possible architectures for sharded clusters. Mongoimport — MongoDB Manual 2.6.6. Synopsis The mongoimport tool provides a route to import content from a JSON, CSV, or TSV export created by mongoexport, or potentially, another third-party export tool.
See the Import and Export MongoDB Data document for a more in depth usage overview, and the mongoexport document for more information regarding mongoexport, which provides the inverse “exporting” capability. Considerations Do not use mongoimport and mongoexport for full instance, production backups because they will not reliably capture data type information.
Extended JSON — MongoDB Manual 2.6.6.