Ingest Shared Concerns
Generally speaking, the following sections ("Mapping DSL," "Queueing System", "Record Processing") represent intimately linked decisions to be made, in that it is impossible to make incompatible choices across the three areas. However, it's interesting to consider each of these independently to avoid prejudicing the discussion by making an early decision in one area and disregarding the other two.
Mapping DSL
Description:
A generalized, easy-to-use language for converting documents of arbitrary schemas into DPLA MAP. At first, this will likely be implemented in a general programing language as part of the Record Processing project, with the expectation that we will eventually deliver a language that metadata experts with little-to-no programming experience will be successful using on their own, or with minimal supervision.
It's expected that this project will primarily be custom code with possibly a number of implementations if it needs to work in mutually-incompatible environments. Therefore, framework exploration probably isn't needed.
Selection criteria:
- Simple to use
- Accessible by non-programmers
- Needs to handle core use cases
- JSON
- XML
- multi-schema/multi-namespace documents
- DPLA MAP (which is JSON-LD)
Nice-to-haves:
- Able to run in a variety of execution contexts (browser, command line, grid computing frameworks)
- Support for some kind of 'live-preview' of DSL transformations, allow for rapid prototyping and development of mappings by non-developers
- Easily usable by partners in their own environments
- Deep document validity checks (not just well-formedness)
- Use of a declarative language like XPath, JSONPath, or XQuery for specification of field sources/destinations
- Use of a framework for creating custom languages starting from some sort of formal specification ala YACC/LLVM, etc.
| Technology Option | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|
| Javascript | JSON is native. DOM manipulations are built-in. Runs in a number of contexts (JVM, Browser, CLI). | Weak typing. Tooling is less mature than other options. Lots of churn in best practices/fashionable libraries. Code executing in native Javascript is not that fast. Not generally used for data munging. | ||
| Python | Explicit but succinct syntax. Very mature. Certain libs built from C can be very fast. Strong typing. Functional. Expressive. Easy to understand at a glance. | Native Python isn't very fast. | Python is strong in the data science community, so potential for crossover to analytics/general data munging. Mark knows it well. | |
| Scala | Nearly as fast as Java while being less verbose. Great type system. XML parsing / navigation / document creation already available in strongly-typed DSLs. Functional. Can run in the browser via ScalaJS. | Because Haskell-type people are working on it, can be overcomplicated. | Also a good data science crossover, but more for data engineering. Michael knows it some. | |
| Java | Fast. Mature. Strong typing. Sorta functional (in 8). | Verbose. Libraries can be overcomplicated or too low-level. Not very expressive. | Michael knows it well. There is some community love for Java (Fedora) | |
| Ruby | We "know" it. Very expressive. | Slow. Attempts to write expressive code yield to unintelligible code. Hard to manage large projects. | Well-loved in the library community. | |
| Go | Fast. Strong typing. Intended for writing concurrent server processes. Designed for easy deployments (executable binaries, statically linked, not interpreted). | No exception handling. Omits some OO features people might be used to. | Nobody on the team knows it. |
Job Queueing System
Description:
The queuing system controls the runtime execution of activities. Currently, Ingestion 2 uses Resque, which is a Ruby-based environment that uses Redis as a datastore and for transaction logic.
Selection criteria:
- Must allow for an Activity's Job to be queued
- Must somehow report statistics about the state of play of an Activity for reporting purposes.
- Must allow for management of failures.
- Must allow for distribution of Jobs among multiple workers.
- We need to be able to support this system, so having an ops playbook ahead of time is a good idea.
Nice-to-haves:
- Choice of implementation languages for workers
- Retrying capabilities
- Broader utility outside of ingestion use cases
- Allow for jobs to be scheduled for a specific date/time
| Technology Option | Worker Language | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|---|
| Airflow | Any | Allows one to model both Activities and individual record operations. Polyglot. Has a built-in management UI. Can handle graphs of dependencies vs. only queues. Can handle retrying tasks. Prebuilt operators for a variety of tasks like dealing with S3, REST endpoints, sending emails. Good takeup by startups doing data operations tasks. Same backends as Celery because it uses it. Simple implementation for what it is. | More complicated than straight up queueing libraries. Larger codebase to understand. | Reusable for other situations where we need to do ETL or other data operations.
| |
| RQ | Python | Management via RQ Dashboard. Uses Redis which we know how to run or can get Amazon to run for us. Simple, understandable implementation. | Single worker language option. Task-only. | ||
| Custom | Any | (We talked about this being a bad idea, but it was up on the whiteboard.) | Have to build it ourselves. Hard to assure correctness. | Ability to say we built a queueing framework? https://www.sadtrombone.com/ | |
| Kafka | Many | Very durable document storage for replay. | More complicated. Doesn't track activities, just worker tasks. Need to run Kafka + Zookeeper. | ||
| Resque | Ruby | Builtin management dashboard. Uses Redis which we know how to run or can get Amazon to run for us. | Single worker language option. Task-only. | We already use it. | |
| Celery | Python | Management via Flower. Multiple brokers on the backend, including Redis and relational dbs. | Single worker language option. Task-only. | ||
| Spark | Python, Java, Scala | Batch oriented. Option to not have standing infrastructure for ingest/map/enrichments. Can run on spots. Incorporates the worker runtime as well. Can run multiple providers on different clusters. Amazon launches Spark clusters as part of EMR. Highly scalable. | Task only. | Michael knows it. |
Record Processing
Description:
An execution environment for running harvests, maps, and enrichments across a provider's contributed metadata.
Selection criteria:
- Speed
- Transaction isolation, record failed != activity failure (should this even be a consideration here? It is a design choice but I'm including it in case there there are tech options that would make this pattern harder to implement)
Nice-to-haves:
- TODO
Notes:
It might not make sense to consider the Record Processing, Mapping DSL, and Queuing System projects separately if they are highly coupled.
| Technology Option | Language | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|---|
Webapp Shared Concerns
As each of the following tech selection sections are related to creating webapps, they share concerns. However, in this case, the decisions are not intimately related; we could very easily make separate decisions in each case.
Dashboard
Description:
The Dashboard is a web application that will allow DPLA staff and partners and hubs to see the status of ingestion, mapping and enrichment processes on their data. It is now the Tech Team's intent that this application will get information about the status of these ingests through a REST API, which means that the Dashboard will be loosely coupled to the Ingestion stack. This will allow for evolution of the implementation and implementation technology of Ingestion without needing to modify the Dashboard application.
Selection criteria:
- TODO
Nice-to-haves:
- TODO
Notes:
The tech selection process for the Dashboard may very well be similar to that of the QA app, with the caveat that the Dashboard app will be built by a third party (HM).
| Technology Option | Language | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|---|
| Rails | Ruby | ||||
| Flask | Python | ||||
| Django | Python | ||||
| Play | Java or Scala |
QA App
Description:
The QA application will allow metadata experts to examine the output of mapping and harvest prior to writing to the production Elasticsearch index.
Selection criteria:
- TODO
Nice-to-haves:
- TODO
| Technology Option | Language | Strengths | Weaknesses | Opportunities | Threats |
|---|---|---|---|---|---|
| Rails | Ruby | Already in our stack ("we know it"). Popular in the community (blacklight, spotlight) | Blacklight is not integrated with ES (we would need to rely on Solr or build the integration with ES) | ||
| Flask | Python | ||||
| Django | Python | ||||
| Play | Java or Scala |
Developers Experience / Interests
| Dev | Expert At | Good At | Familiar With | Wants to Learn |
|---|---|---|---|---|
| Audrey | HTML+CSS, Javascript for DOM manipulations, Ruby (in Ruby on Rails context) | Object oriented Javascript, PHP (a little rusty), Ruby, SQL | Python, Java | Python, Scala, Java |
| Mark | Unix, Python(was pretty confident, now a little rusty), Javascript, PHP(formerly, doesn't like), HTML+CSS(a little rusty), Perl(rusty, been a while, is so over that) | Ruby | C, Java | Go, more Python, Scala, Java, Natural Language Processing |
Michael | Java, XML, Solr, Hadoop | Scala, Ruby (mostly not Rails) | Python, Javascript, Perl, C, Objective-C, XSLT, Spark, NLP, Machine Learning, Elasticsearch, Redshift, | Python, more Scala, Spark, |
| Scott | Not claiming "expert" skills in these subject but its what I'm strongest at. Java (<1.7), SQL (MSSSQL), Solr | Ruby (still learning), Python | Elasticsearch, Django (the only web framework I've do work with), C++ (bloodshed days) | Java 1.8, Scala, Spark, Go |