Welcome to the source repository for various code references found in several FIS Advanced Technology white papers.
In December 2015, FIS Advanced Technology released the white paper "Transforming Options Market Data with the Dataflow SDK", to provide software engineers insight into Google Cloud Dataflow's optimal programming model and execution environent.
In August of 2016, FIS Advanced Technology produced a sequel to the original 2015 Bigtable whitepaper, detailing the introduction of Google Cloud Dataflow and Google Cloud BigQuery to the Market Reconstruction Tool's solution architecture, as well as to provide a deeper look into the material covered at our Analyzing 25 billion stock market events in an hour with NoOps on GCP talk from Google NEXT 2016.
September of 2016 saw the release of "Market Reconstruction 2.0: Visualization at Scale", illustrating the team's experience designing the user interface for a securities transaction regulatory database expected to grow to 35 petabytes over 6 years.
- Market Reconstruction 2.0: A Financial Services Application of Google Cloud Bigtable and Google Cloud Dataflow
- Market Reconstruction 2.0: Visualization at Scale
- Transforming Options Market Data with the Dataflow SDK
- Scaling to Build the Consolidated Audit Trail: A Financial Services Application of Google Cloug Bigtable
To build the project:
mvn clean install
Out of the box, the repository is configured to run a standalone Dataflow job on the local workstation, using input data that ships with the repository (input/zvzzt.input.txt).
The example can be run locally either executing:
cd bin && ./run
or by calling Maven with:
mvn clean install && mvn -Plocal exec:exec
Once you have activated a Google account on Google Cloud Platform, you will need your Project ID and at least one GCS bucket to be created (for storing deployment artifacts and input files.)
Log your shell into GCP:
gcloud auth login
If you do not already have a Google Cloud Storage bucket, you can create one with the following command:
gsutil mb gs://<pick_a_bucket_name>
Copy input specimen to Google Cloud Storage:
gsutil cp input/zvzzt.input.txt gs://<pick_a_bucket_name>
Ensure that there is a proper destination dataset in your BigQuery account. For example, this command will create a dataset called dataflow-project within BigQuery for your account:
bq mk <dataflow_project>
Execute the following, substituting your own values PROJECT and BQDEST in bin/run:
cd bin && ./run gs://<pick_a_bucket_name>/zvzzt.input.txt
The Pipeline will automatically create the table if it does not exist, although it cannot create the initial dataset.
To execute the job upon Google Cloud Platform using Maven, edit the associated values for your project ID and account within pom.xml and then run:
mvn clean install && mvn -Pgcp exec:exec
Remember that you can not use local files but have to use files stored from/to GCS (gs://).
Please open up a GitHub issue for any discrepancies or inconsistencies you may discover and we will correct and publish here.
MIT. See license text in LICENSE.
Copyright © FIS 2016. Licensed under the MIT license.
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