Apache Lucene is an open source and free text search engine library written in Java. It is a technology suitable for applications that requires full-text search, and is available cross-platform.
Apache Lucene is a perfect text search implementation where the heap space usage needs to be kept to its minimal. It also enables search based on various search fields and most importantly the search and index process can happen simultaneously. The only scenario where it might be less appropriate would be when the index size grows too big. We have witnessed few scalable issues where the search would take a while when the index size is too large.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
We found Apache Lucene to be extremely performant in querying large amounts of data and retrieving the correct files based on the metadata provided.
The online community offers great support for the product. Even though it is an open source tool, it is not difficult to find help online for it.
When we were creating a proof of concept application, we found that the software worked just as well, while being run locally on a resource-limited PC.
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
The search and index performance of [Apache] Lucene is excellent and the quality of results is good, if not better. For implementing it with small scale applications it is a no brainer, Lucene is the best and most cost effective solution. Learning curve is not too steep either.
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Being an open source project we did not have to pay any licensing fees for using Apache Lucene. It has greatly improved our search functionality in our web apps.
Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
