What is the trend of a specific topic, such as a new brand or a current issue happening somewhere in the world? This post shows how to answer this question, and several similar, using a streaming pipeline and an analytic dashboard powered by Twitter Streaming API, Solr, Logstash and Banana.
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Clustering is one of the most common unsupervised Machine Learning tasks. Solr is shipped with a clustering module based on Carrot2 built-in algorithms. Carrot2 comes with 4 algorithms: Lingo, STC, kMeans and Lingo3D each one mapped to a clustering engine. The first three are open-source whereas the last one is commercial. When this approach is used, clustering takes place in memory. Other frameworks, such as Mahout, can be used to do the clustering “off-line.”
Logs are everywhere and usually generated in large sizes and high velocities. These logs can be used to obtain useful information and insights about the domain or the process related to these logs, such as platforms, transactions, system users, etc. In this post, a realtime web (Apache2) log analytics pipeline will be built using Apache Solr, Banana, Logstash and Beats containers.
However, in order to get the pipeline running, several integration aspects related to streaming data need to be addressed through settings and patches supplied through mounted volumes. The structure of these volumes can be as below:
A container is an abstraction layer to run a software application in a lightweight environment. Containerization provides a standard and a secure way to build, ship and run applications anywhere. Docker images of Solr and Banana are available for quick installation and run.
Graph traversal features have been introduced in Solr 6 releases. These powerful features enables Solr users to run expressions that traverses graph structures in order to introduce or extract useful information. These graph traversal features are particularly useful when data is already indexed into Solr and light graph operations are required especially on top of text search. Before proceeding, a basic knowledge of Solr and graph structures is required.
Solr traversal implementation uses Breadth First Search (BFS) to perform graph traversal which is more suitable for solving search problems than its counterpart Depth First Search (DFS). It is also possible to combine graph traversal with other search or streaming operations.
In this post, we are going to explore basic graph visualization introduced in Banana v1.7. To visualize a graph in Banana, there must be at least a collection indexed into Solr with two fields: from and to that represent the adjacency matrix or, in other words, the edges of the graph. Alternatively, two collections can be used to visualize the graph: a main collection which is configured in the dashboard settings and an additional graph collection that stores the graph matrix. The main collection will be joined with the graph collection to retrieve node labels.
TL;DR if you have a raw dataset or a data indexed into Apache Solr, a meaningful analytics dashboard that gives insights and useful graphical and tabular information can be built in minutes.