Introduction

In this part is presented a detailed description of the available CogStack examples, which currently are:

• Example 1 – Processing a simple, structured dataset from a single DB source.

• Example 2 – Processing a combined structured and free-text dataset from a single DB source (as in Quickstart).

• Example 3 – Processing a combined dataset from multiple DB sources, multiple jobs.

• Example 4 – Processing a combined dataset with embedded documents from a single DB source.

• Example 5 – 2-step processing of a combined dataset with embedded documents from a single DB source.

• Example 6 – Basic security use-case: Example 2 extended reverse proxy enabling a secure access.

• Example 7 – Logging: Example 6 extended with logging mechanisms.

• Example 8 – Simple NLP use-case: drug annotation using GATE and based on Example 2.

• Example 9 – Defining multi-component pipelines: Example 4 and Example 8 combined.

• Example 10 –  Ingesting free-text data from DB source to ES, annotating with MedCAT and re-ingesting back to ES. 

• Sample production deployment - structured project setup of files to be used for production deployments

The main directory with resources used in this tutorial is available in the the CogStack bundle under examples directory.

Some parts of this document are also used in CogStack Quickstart tutorial.

How are the examples organized

Each of the examples is organized in a way that it can be deployed and run independently. The directory structure of examples/ tree is as follows:

Common and reusable components

The directory docker-common contains some common components and microservice configuration files that are used within all the examples. These components include:

• PostgreSQL databases: pgsamples and pgjobrepo directories,

• ElasticSearch node: elasticsearch directory,

• Kibana webservice dashboard: kibana directory,

• nginx reverse proxy service: nginx directory,

• Fluentd logging driver: fluentd directory,

• Common microservices Docker Compose base configuration file used across examples: docker-compose.yml.

Examples

The directories example* stores the content of the examples, each containing such subdirectories:

• cogstack directory containing CogStack configuration files and/or custom pipeline scripts,

• db_dump directory containing database dumps used to initialize the samples input database,

• docker directory containing configuration files for docker-based deployment,

• extra directory containing scripts to generate database dumps locally (optional) or additional, useful materials,

• setup.sh script to initialize the example before running it for the first time.

For a detailed description of each example please refer to its appropriate section.

Raw data

The directory rawdata contains the raw EHRs data which will be used to prepare the initial database dumps for running the examples. The datasets used in all the examples are the ones used in the Quickstart guide – please refer to it for a more detailed description of the available datasets.

Data preparation scripts

The script prepare_docs.sh is used to prepare the document data for Example 4 and Example 5 in PDFs, DOCX and image formats.

The script prepare_db_dumps.sh is used to prepare locally all the database dumps to initialize the examples

However, the script download_db_dumps.sh is used to automatically download all the database dumps.

General information about examples

Database schema

In the current implementation, CogStack can only ingest EHR data from a specified input database and relation. This is why, in order to process the sample patient data covered in this tutorial, one needs to create an appropriate database schema and load the EHR data into a preferred data sink. The data can be stored either as records in a database, documents in an ElasticSearch cluster or as files in a filesystem.

The most commonly used data sink is going to be ElasticSearch cluster (in our examples – just a single node). However, as relational join statements have a high performance burden for ElasticSearch, the EHR data is best to be stored denormalized in ElasticSearch. This is why, for the moment, we rely on ingesting the data from additional view(s) created in the sample database.

Properties file

Database source

When using PostgreSQL database as a data source, please note that the source.poolSize property defines the maximum size of the connection pool available for performing queries by CogStack engine. A PostgreSQL database, by default, has a maximum connection limit set to 100, hence exceeding the limit (either by a single job or multiple parallel ones) may lead to termination of the data pipeline.

One of the solutions to overcome this issue can be to run the PostgreSQL container with additional options specified in Docker-compose file:

command: "-c 'shared_buffers=256MB' -c 'max_connections=1000'"

extending the connection limit with the available RAM for connection buffers.

ElasticSearch sink

As an additional feature, security and ssl encryption can be enabled for communication with ElasticSearch. However, it uses the ElasticSearch X-Pack bundle and requires license for commercial deployments, hence it is disabled by default.

Moreover, the ElasticSearch and Kibana images that are used in the tutorial are not bundled with X-Pack, using the -oss variant of the image as defined in examples/docker-common/docker-compose.yml .

Partitioner

In the current implementation, CogStack engine can only partition the data using the records’ primary key (partitioner.pkColumnName property containing unique values) and records’ update time (partitioner.timeStampColumnName property) as defined in created views. Also, the table used to partition (partitioner.tableToPartition property) needs to be specified. This is specified by PKTimeStamp partitioning method as shown below, e.g.:

partitioner.partitionType = PKTimeStamp
partitioner.tableToPartition = <table-name> partitioner.pkColumnName = <primary-key-column-name>
partitioner.timeStampColumnName = <timestamp-column-name>

Running CogStack

Setup

For the ease of use CogStack is being deployed and run using Docker. However, before starting the CogStack ecosystem for the first time, a setup scripts needs to be run locally to prepare the Docker images and configuration files for CogStack data processing engine. For each of the examples, a script is available in its directory examples/example*/ path and can be run as:

bash setup.sh

As a result, a temporary directory __deploy/ will be created containing all the necessary artifacts to deploy CogStack.

Docker-based deployment

Next, we can proceed to deploy CogStack ecosystem using Docker Compose. It will configure and start microservices based on the provided Compose files:

• common base configuration, copied from examples/docker-common/docker-compose.yml ,

• example-specific configuration copied from examples/example*/docker/docker-compose.override.yml.

Moreover, the PostgreSQL database container comes with pre-initialized database dump ready to be loaded directly into.

In order to run CogStack, type in the examples/example*/__deploy/ directory:

docker-compose up

In the console there will be printed status logs of the currently running microservices. For the moment, however, they may be not very informative (we’re working on that).

Connecting to the microservices

CogStack ecosystem

The picture below sketches a general idea on how the microservices are running and communicating within a sample CogStack ecosystem.

Open

Assuming that everything is working fine, we should be able to connect to the running microservices.

Kibana and ElasticSearch

Kibana dashboard used to query the EHRs can be accessed directly in browser via URL: http://localhost:5601/. The data can be queried using a number of ElasticSearch indices, e.g. sample_observations_view. Usually, each index will correspond to the database view in db_samples (samples-db PostgreSQL database) from which the data was ingested. However, when entering Kibana dashboard for the first time, an index pattern needs to be configured in the Kibana management panel – for more information about its creation, please refer to the official Kibana documentation.

In addition, ElasticSearch REST end-point can be accessed via URL http://localhost:9200/. It can be used to perform manual queries or to be used by other external services – for example, one can list the available indices:

curl 'http://localhost:9200/_cat/indices'

or query one of the available indices – sample_observations_view:

curl 'http://localhost:9200/sample_observations_view'

For more information about possible documents querying or modification operations, please refer to the official ElasticSearch documentation.

As a side note, the name for ElasticSearch node in the Docker Compose has been set as elasticsearch-1. The -1 ending emphasizes that for larger-scale deployments, multiple ElasticSearch nodes can be used – typically, a minimum of 3.

PostgreSQL sample database

Moreover, the access PostgreSQL database with the input sample data is exposed directly at localhost:5555. The database name is db_sample with user test and password test. To connect, one can run:

psql -U 'test' -W -d 'db_samples' -h localhost -p 5555

Example 1

General information

This is a very basic example covering only processing and ingestion of structured synthetic data into ElasticSearch. However, it forms a good base for starting to work with CogStack data processing pipelines.

Database schema

Patients table

The first 5 records of patient data (file: patients.csv from Synthea-based samples) in CSV format is presented below:

The patients table definition in PostgreSQL according to the specification:

Encounters table

Similarly, the first 5 records of patient encounters data (file: encounters.csv)

with the corresponding encounters table definition:

Observations table

The next table is observations, where the first 5 rows of observations.csv file are:

and the corresponding table definition:

Here, with -- (*) have been marked additional fields with auto-generated values. These are: cid – an automatically generated primary key and created – a record's creation timestamp. They will be later used by CogStack engine for data partitioning when processing the records. The patient and encounters tables have their primary keys (id field) already defined (of UUID type) and are included in the input CSV files.

Database views

Next, we define a observations_view that will be used by CogStack data processing engine to ingest the records from input database:

The goal here is to denormalize the database schema for CogStack and ElasticSearch data ingestion, as the observations table is referencing both the patient and encounters tables by their primary key. In the current implementation, CogStack pipeline engine cannot yet perform dynamic joins over the relational data from specific database tables.

Some of the crucial fields required for configuring CogStack Pipeline engine with Document data model have been marked with --(*) – these are:

• observation_id – the unique identifier of the observation record (typically, the primary key),

• observation_timestamp – the record creation or last update time.

These fields will be later used when preparing the properties configuration file for CogStack data processing workflow.

Properties file

General information

Each CogStack data processing pipeline is configured using a number of parameters defined in the corresponding Java properties file. In this example we use only one pipeline with configuration specified in examples/example1/cogstack/observations.properties file.

Spring profiles

CogStack configuration file uses a number of Spring profiles, which enable different components of the data processing pipeline. In this example we have:

spring.profiles.active = jdbc_in,elasticsearchRest,localPartitioning

which denotes that only such profiles will be active:

• jdbc_in for JDBC input database connector,

• elasticsearchRest for using REST API for inserting documents to ElasticSearch,

• localPartitioning functionality.

Data source

The parameters for specifying the data source are defined as follows:

source.JdbcPath = jdbc:postgresql://samples-db:5432/db_samples source.Driver = org.postgresql.Driver source.username = test source.password = test

In this example we are using a PostgreSQL database which driver is defined by source.Driver parameter. The PostgreSQL database service is available in the CogStack ecosystem as samples-db, has exposed port 5432 for connections and the sample database name is db_samples – all these details need to be included in the source.JdbcPath parameter field.

Next, we need to instruct CogStack engine how to query the records from the data source:source.selectClause = SELECT *

source.fromClause = FROM observations_view source.sortKey = observations_id

source.primaryKeyFieldValue = observations_id
source.timeStamp = observations_timestamp

source.dbmsToJavaSqlTimestampType = TIMESTAMP

This is where the previously defined observations_view with additional CogStack-specific fields are used.

Data sink

Next, we need to define the data sink – in our example, and by default, ElasticSearch is being used:

elasticsearch.cluster.host = elasticsearch-1 elasticsearch.cluster.port = 9200

Similarly, as when defining the sample database source, we need to provide the ElasticSearch host and port configuration according to the microservices definition in the corresponding Docker Compose file (see examples/example1/docker/docker-compose.override.yml).

In the next step, we specify some of the optional ElasticSearch indexing parameters:

elasticsearch.index.name = sample_observations_view elasticsearch.excludeFromIndexing = observations_id

We specify the index name which will be used to store the documents processed by CogStack engine. Additionally, we specify which fields should be excluded from the indexing – by default, we usually exclude the binary content, the constant-value fields and the primary key from the observations_view.

Jobs and CogStack engine configuration

CogStack engine in order to coordinate the workers needs to keep the information about the current jobs in an additional PostgreSQL database – cogstack-job-repo. Hence, similarly as when defining the source database, this database needs to specified:

jobRepository.JdbcPath = jdbc:postgresql://cogstack-job-repo:5432/cogstack jobRepository.Driver = org.postgresql.Driver jobRepository.username = cogstack jobRepository.password = mysecretpassword job.jobName = job_observations_view

The last parameter job.jobName is a default name for the jobs that will be created.

Partitioner and scheduler

Another set of useful parameters are related with controlling the job execution and data partitioning:

partitioner.partitionType = PKTimeStamp
partitioner.tableToPartition = observations_view partitioner.pkColumnName = observations_id partitioner.timeStampColumnName = observations_timestamp

Apart from data partitioning, although optional, it can be useful to set up the scheduler – the following line corresponds to the scheduler configuration:

scheduler.useScheduling = false

In this example we do not use the scheduler, since we ingest EHRs from the data source only once. However, in case when data is being generated in a continuous way, scheduler should be enabled to periodically run CogStack jobs to process the new EHRs. It is disabled by default.

Deployment information

This example uses the standard stack of microservices as presented in Running CogStack based on the Docker Compose files: examples/docker-common/docker-compose.yml and  examples/example1/docker/docker-compose.override.yml.

The current example also uses a single CogStack properties file (see examples/example1/cogstack/observations.properties) and hence runs only one instance of CogStack data processing engine.

Example 2

General information

This example is an extension of Example 1. Apart from containing structured synthetic data, it also contains free-text documents data, hence creating a combined dataset.

This example is also covered as a main part of CogStack Quickstart tutorial.

Database schema

The database schema is almost the same as the one defined in Example 1. The only difference is an additional column in encounters table, as presented below.

Encounters table

The encouters table definition:

Here, with -- (*) has been marked an additional document column field of TEXT type. This extra field will be used to store the content of a document from MTSamples dataset.

Just to clarify, Synthea-based and MTSamples datasets are two unrelated datasets. Here, we are extending the synthetic dataset with the clinical documents from the MTSamples to create a combined one, to be able to perform a bit more interesting queries.

A sample document from MTSamples dataset is presented below:

Database views

Analogously, the new document column field is included in the observations_view, where the view is based on the one defined in Example 1.

Properties file

The properties file used in this example is the same as in Example 1.

Deployment information

Similarly as in Example 1, this one uses the standard stack of microservices defined in examples/example2/docker/docker-compose.override.yml.

It also uses a single CogStack properties file and hence runs only one instance of CogStack data processing engine (see: examples/example2/cogstack/observations.properties).

Example 3

General information

This example covers a case of multiple data sources and multiple CogStack instances scenario. This example is a further extension of both Example 1 and Example 2. It extends Example 1 by defining schema for all the tables for Synthea-based patient data. It also extends Example 2 by defining a separate table for representing and storing MTSamples data.

Database schema

The database schema is based on the one defined in Example 1 where the same definition logic has been applied to the rest of CSV files available in the synthetic dataset. The complete database schema for the synthetic data is available in examples/example3/extra/db_create_syn_schema.sql file.

The only new table is the one for representing MTSamples data defined in examples/example3/extra/db_create_mt_schema.sql.

Samples table and view

The definition of mtsamples table and its corresponding view:

In contrast to MTSamples data representation used in Example 2 (where the full content of a document was stored in the document field in the encounters table), in this example we partially parse the document, hence improving the data representation.

Two additional fields have been added to connect with CogStack Document model:

• cid – automatically generated unique id,

• dct – a document creation timestamp.

Please refer to examples/example3/extra/prepare_synsamples_db.sh on how the synthetic data is parsed and examples/example3/extra/prepare_synsamples_db.sh on how the MTSamples data is parsed.

Properties files

The properties files used in this example are based on the one from Example 1. However, since multiple views are defined, the properties files can be automatically generated based on a provided template.properties file by running in examples/example3/cogstack/ directory:

This way, each generated properties file corresponds to an individual view as defined in the database schema.

Apart from that, a separate mt.properties file is provided for processing MTSamples data as defined in samples_view.

Deployment information

This example uses the standard stack of microservices (see: Running CogStack), but extended with additional database storing input sample data. It uses 2 separate input databases as the data source: samples-db and mtsamples-db – see: examples/example3/docker/docker-compose.override.yml.

It also uses multiple CogStack properties files, hence multiple instances of CogStack data processing engine are run, one per each properties file.

Example 4

General information

This example covers a common use-case of processing and parsing document data that is stored alongside records in the database. This example is based on Example 2 and extends the CogStack data processing workflow by including Apache Tika module as one of the core components used for processing documents.

Data preparation

Apart from structured records data in text format, this example uses documents which are loaded directly into the input database. The script for generating documents prepare_docs.sh is provided in the examples main directory.

At the moment, the following use-cases have been prepared with documents generated in the following formats:

• docx – documents in DOCX format of text type,

• pdf-text – documents in PDF format of text type,

• pdf-img – documents in PDF format of image type,

• jpg – documents in JPEG format.

Each use-case can be seen as a standalone example, but using the same database schema, CogStack properties file and docker-compose file. Hence, when generating docker-based deployment data (by running setup.sh script), a separate directory will be generated.

Database schema

The database schema is based on the one defined in Example 2. Only some minor modifications were made in encounters table and observations_view – see: examples/example4/extra/db_create_schema.sql file.

Encounters table

In this example, the document is stored in column binarydocument of BYTEA type – instead of DOCUMENT as raw TEXT defined in Example 2.

Observations view

The important field added in this view is encounter_binary_doc which will be used to read the binary document by CogStack engine.

Properties file

The properties file used in this example is based on the one from Example 2, but extended with parts covering Tika documents processor and which are covered below.

Spring profiles

The spring profile part has been updated with adding a tika profile:

spring.profiles.active=jdbc_in,elasticsearchRest,tika,localPartitioning

Tika configuration

A new part covering Tika processing has been added:

tika.tikaFieldName = tika_output tika.binaryContentSource = database tika.binaryFieldName = encounter_binary_doc

The property tika.tikaFieldName denotes the name of the key field tika_output. This field will be present in the output JSON file where the value will hold the content of the Tika-parsed document.

The property tika.binaryContentSource defines the source where the documents are stored – in our case: database. Following, the property tika.binaryFieldName denotes the name of column that contains binary document data – in our case that is encounter_binary_doc field in observations_view view.

It’s important to note that the remaining information about mapping and querying of the source database tables and record fields are covered by source.* properties, as explained in Example 1.

Deployment information

When running setup.sh script, a number of separate directories will be created, each corresponding to a document format use-case.

Apart from that, this example uses the standard stack of microservices (see: Running CogStack) and also uses a single CogStack properties file, running only one instance of CogStack data processing engine.

Example 5

General information

This example covers a bit more complex use-case of processing and parsing EHR data where the documents are stored alongside records in the same database. This example is based both on Example 3 and Example 4, with the difference that only a single job is being run and the data processing workflow is divided into two steps:

1. Pre-processing and parsing of the documents data,

2. Processing all the records.

Usually, we would like to perform the steps (1) and (2) in the same workflow as presented in Example 4. However, there may be cases that we may prefer to pre-process the data and store it temporarily prior to ingestion into ElasticSeach (or other data sink). This may be a solution for cases where one would like to have more control on the documents parsing process, e.g., to possibly easily re-launch parsing of the selected or failed documents.

Database schema

The database schema is based on the one from Example 3 with some minor modifications and with additional tables introduced.

Encounters table

In the encounters table a representation of the document data has been altered:

In this example, we only store the ID of the document in documentid field.

Medical reports – binary documents

Next, we define medical_reports table on a similar basis as samples table used in Example 3:

In this example, the document is stored in binary format in binarydoc field. Similarly, as in Example 4, this column will be used to access the binary content of the document.

Medical reports – processed documents

Next, we define medical_reports_processed that will be used to store the processed data generated by the fist step of the CogStack pipeline:

The field output will contain the output of Tika documents processor in JSON format. It’s important to note that cid field references the medical_reports table so that the document data will remain properly linked.

Following, we define an optional reports_processed_view which can be used to query the processed documents or to check the documents processing status:

As mentioned previously, the output content is stored in JSON format, hence we need to access the information by key-value parsing the JSON content. There are more fields available for querying, however, in this simple example we only focus on:

• tika_output – a field that contains the Tika-parsed document,

• X-PDFPREPROC-OCR-APPLIED – a field that contains the status of OCR processor (if it was applied).

This view is optional, but can be used for debugging purposes.

Observations view

Finally, we define observations_view which will be used in the final step to ingest the records data into ElasticSearch:

The view allows to query the patient data as in the previous examples. The querying of Tika-parsed document content is defied on a similar basis as in reports_processed_view. The view also allows to query for the information included with the original document, but skipping the binary content.

Properties files

For each data processing step, a separate properties file is provided.

Step 1 – documents pre-processing using Tika

The properties files used in this step is based on Example 4 – see examples/example5/cogstack/conf/step-1/reports.properties for the definition. However, as a data sink it is using a PostgreSQL database (actually, the same as the source one).

Spring profiles

The spring profiles used in this step are:

spring.profiles.active=jdbc_in,jdbc_out,tika,localPartitioning

In general, this tells us that the documents will be read from an input database (profile: jdbc_in), processed using tika with localPartitioning scheme and stored in an output database (profile: jdbc_out).

Data source and data sink

The source and target database are specified as follows (in fact, it's the same database):

source.JdbcPath = jdbc:postgresql://samples-db:5432/db_samples source.Driver = org.postgresql.Driver source.username = test source.password = test target.JdbcPath = jdbc:postgresql://samples-db:5432/db_samples target.Driver = org.postgresql.Driver target.username = test target.password = test

The data source and target binding for CogStack engine is defined as follows:

source.primaryKeyFieldValue = cid source.timeStamp = dct source.selectClause = SELECT * source.fromClause = FROM medical_reports source.sortKey = cid
target.Sql = INSERT INTO medical_reports_processed (cid, dct, output) VALUES ( CAST( :primaryKeyFieldValue AS integer ), :timeStamp, :outputData )

In this first data processing step we are going to read the data from medical_reports – as provided for source.fromClause.

Moreover, we also need to define the INSERT clause for the property target.Sql which tells CogStack engine how to write the processed documents into the target database. This is required when using database as a data sink.

Tika configuration

tika.binaryFieldName = binarydoc tika.tikaFieldName = tika_output tika.binaryContentSource = database

The property tika.tikaFieldName denotes the name of the key field tika_output in the output JSON file where the value will contain the content of the Tika-parsed document. See, e.g., the reports_processed_view where the content of tika_output is accessed and parsed.

The property tika.binaryFieldName denotes the name of the column that contains the binary document data – in our case it is the binarydoc field in medical_reports view.

Step 2 – records ingestion into ElasticSearch

The properties file used in this example is similar to the one from Example 1 – see examples/example5/cogstack/conf/step-2/observations.properties for the definition. It is a pretty basic one and won’t be covered here.

Deployment information

When running setup.sh script, a number of separate directories will be created, each corresponding to a document format use-case.

Apart from that, this example uses a standard stack of microservices and runs only one instance of CogStack data processing engine. However, since this example implements 2-step processing, two CogStack instances are run, but in a sequential manner. Firstly, CogStack pipeline is executed using reports.properties configuration file and after that it is run with supplied observations.properties file.

Example 6

General information

This example is based on Example 2 which extends it with basic security mechanisms implemented by Nginx – a web service that can be used as a load balancer, reverse proxy or HTTP cache. In this example, Nginx will be used as a reverse proxy providing a security layer used to connect to running microservices in CogStack ecosystem.

Deployment information

To deploy the example (after running setup.sh script) just type in __deploy/ directory as usual:

docker-compose up

Assuming that everything is working fine, we should be able to connect to the running microservices as shown in Example 2. When accessing webservices and when asked for credentials the username is test with password test.

This example uses the standard stack of microservices (see: Running CogStack), but extended with Nginx reverse proxy service and internal network. For a better control and isolation of the services, in Docker Compose file (examples/example6/docker/docker-compose.override.yml) we defined 2 networks: esnetand public. The esnet network will be used as a internal, private network for the data processing pipeline and services – the access should be highly restricted. The public network will be used as a bridge to connect the services to the outside host. When deployed, Nginx will be running as an additional microservice in the CogStack ecosystem using both esnet and public networks. It will control the communication between selected running microservices (elasticsearch-1 and kibana) and the outside world. The only one difference here is the samples-db database service, which for debugging purposes is using both networks and have ports directly exposed and bound to localhost:5555.

The picture below illustrates such deployment scenario.

Open

Nginx

Security

As mentioned previously, in this example, Nginx is used only as a reverse proxy service providing a simple security layer used to connect the running microservices inside the private network to the outside world. It implements a simple secure HTTP access to kibana and elasticsearch-1 services running at 5601 and 9200 ports respectively. Configured with Docker Compose file, all the HTTP traffic coming to the host on the specified ports will be forwarded to the respective microservices running inside private network through Nginx.

Configuration file

In this example, Nginx only implements basic HTTP access authentication. The authentication is based on a preconfigured .htpasswd file, as specified in Nginx configuration file examples/docker-common/nginx/conf/nginx.conf. The .htpasswd file with login credentials test:test will be automatically created when running setup.sh script. Although the security is very basic here, it can be easily extended to handle other protocols and access patterns. For more information about possible Nginx security configurationsm, please refer to the official Nginx documentation.

ElasticSearch X-Pack Security

In this example, Nginx provides just a basic security layer for the running microservices and is free to use. However, for a more advanced functionality related with secure access to ElasticSearach microservices stack, the X-Pack Security module can be considered. X-Pack Security, although requiring a commercial license, offers functionality, such as:

• user authentication inside ElasticSearch and Kibana,

• role-based and attribute-based control for the data access,

• field- and document-level security,

• encryption of the communication between ES nodes.

For a detailed list of features, please refer to the official ElasticSearch X-Pack documentation.

Example 7

General information

This example is an extension of Example 6 providing logging mechanism using Fluentd log collector and it only focuses on the logging part.

Deployment information

This example uses the stack of microservices used in Example 6, but extending it with Fluentd logging driver. When deployed, Fluend will be running as an additional microservice in order to collect and filter logs from the ones running in CogStack ecosystem. The picture below illustrates such deployment scenario.

Open

Regarding Docker Compose configuration file, for each microservice used an additional section has been added regarding logging – e.g., in case of CogStack pipeline:

cogstack-pipeline: image: cogstacksystems/cogstack-pipeline:latest ... logging: driver: "fluentd" options: tag: cog.java.engine

"fluentd" is used as the logging driver. All the messages from the cogstack microservice will be forwarded to the fluentd driver using cog.java.engine as tag. The directory with the output logs from fluentd running container will be mapped to a local path in the deployment directory: examples/example7/__deploy/__logs. For the full configuration of running microservices, please refer to examples/example7/docker/docker-compose.override.yml.

Fluentd

Custom image

In our setup, Fluend needs some additional filter plugins to be installed, hence a custom Fluentd image is used (as specified in the Docker Compose file). This image is available to download directly from CogStack Dockerhub under the name cogstacksystems/fluentd. Alternatively, the image can be build locally using the Dockerfile in the directory docker-cogstack/fluentd in the CogStack package.

Configuration file

Fluentd uses configuration files to define the filtering and output rules for messages coming from predefined source(s). In our current setup, the Fluentd driver is listening at localhost on port 24224 for the incoming messages, as defined both in the Docker Compose file and Fluentd configuration files. The default configuration file for Fluentd is examples/docker-common/fluentd/conf/fluent.conf . However, here we won’t go too much into logging configuration details and just cover the most important bits.

In this example, all the running microservices output the messages to standard output or standard error. However, not all the messages will be displayed to the end-user. They are firstly forwarded to fluentd driver using a separate tag per each microservice as defined in the Docker Compose file. The general rule applied here is that all the messages which have been previously sent to stderr will be classified as error messages, where as the ones sent to stdout – as informative ones.

After filtering and parsing, all the messages are output into files, separate per each service tag. The logs containing only error messages are stored in files starting with error.* prefix, whereas the full logs are stored in files starting with full.* prefix. The log files are managed using log rotating policy, keeping new logs for 24 hours, before being archived.

As a side note, since the error messages are of high importance, on arrival they are additionally printed to the standard output for the user’s instant inspection.

Output logs format

The logs are output to files in JSON format and they contain such fields:

• container_id and container_name of the running microservice,

• log message,

• time of the message arrival.

To parse the logs, one easy way is to use jq – a flexible JSON command-line processor. For example, to parse the log message field, one may use:

jq ".log" example7/__deploy/__logs/<filename>.log

where <filename> is the filename of a sample log file.

Example 8

General information

This example covers a simple use-case of running NLP applications as one of the components of the data processing pipeline. The example application is using GATE suite as the NLP engine. The database schema used in this example is based on Example 2.

Example GATE application

CogStack pipeline allows to include custom user GATE applications as one of the data processing components. These applications can be previously designed in GATE suite (e.g., using GATE Developer GUI application) and stored as a custom gapp or xgapp application with the used resources. CogStack implements GateService (see: CogStack Pipeline) which uses GATE API to run these applications using the GATE Embedded version.

GATE ANNIE Gazetteer

In this example, we developed a simple GATE application to annotate common drugs and medications. The application is using the GATE ANNIE plugin (with default configuration), implementing a custom version of ANNIE Gazetteer. The application was been created in GATE Developer studio and exported into gapp format. This application is hence ready to be used by GATE and is stored in gate/app directory as drug.gapp alongside the used resources.

The list of drugs and medications to annotate is based on a publicly available list of FDA-approved drugs and active ingredients. The data can be downloaded directly from Drugs@FDA database. The list of used drugs is stored as drug.lst and active ingredients as active.lst.

More information about creating custom GATE applications can be found in the official GATE documentation.

Data preparation

This example is a basic one as our NLP application is not fully context aware and is looking in the text only for the words found in the provided list of drugs. Moreover, as the list of drugs and active ingredients can contain full names of drugs (or ingredients), the full names of drugs may not be found in the parsed text. Hence, the FDA-approved list of drugs and components needs to be further post-processed and filtered. All this is being performed by script extra/clean_list.py in the example directory. It uses as an input the raw data downloaded from Drugs@FDA database and a list of words to filter out (e.g., the most frequent 10000 words in English) to prepare a curated list of drugs and active ingredients.

Properties file

The properties file used in this example is based on the one from Example 2, but extended with parts using GATE documents processor and which are covered below.

Spring profiles

The spring profile part has been updated with adding a gate profile:

spring.profiles.active=jdbc_in,elasticsearchRest,gate,localPartitioning

GATE configuration

A new part covering documents processing using a custom GATE application has been added:

gate.gateHome = /gate/home/ gate.gateApp = /gate/app/drug.gapp gate.fieldsToGate = encounter_document gate.gateAnnotationTypes = Drug gate.gateFieldName = gate

The property gate.gateHome denotes the home directory of GATE application, which should be the same for all GATE applications when using CogStack GATE image from Dockerhub (please see below). gate.gateApp denotes the name of the GATE application to be run – in our example, the application directory (containing the gapp application with resources) will be directly mounted into CogStack container into /gate/app/ directory.

The property gate.fieldsToGate specifies the name of the field from the input database table that contains the text to be processed by the GATE application. The property gate.gateAnnotationTypes specifies the annotations to be extracted (available in the GATE application). Finally, the property gate.gateFieldName defines the custom name of the key in the resulting JSON file (or output table) under which the extracted annotations will be stored.

Deployment information

When running setup.sh script, a number of separate directories will be created. Since NLP components for pipelines require additional applications to be installed in the system, CogStack provides another, extended image containing them. In this example, hence cogstacksystems/cogstack-pipeline-gate:latest image is being used.

Apart from that, this example uses the standard stack of microservices and also uses a single CogStack properties file, running only one instance of CogStack data processing engine.

Example 9

General information

This example covers a use-case of running a pipeline of multiple components combined together:

• extraction of records from the input database that additionally contain free-text documents in PDF format,

• text extraction from the PDF document using Apache Tika,

• annotation extraction using GATE suite,

• generating output documents in JSON format and storing them in ElasticSearch sink.

The example is based on Example 4, which uses Apache Tika to extract text from PDF documents and on Example 8 which uses simple GATE NLP application to annotate drugs and active ingredients in medical text.

Properties file

The properties file used in this example is based on both Example 4 and Example 8. The Spring active profiles used are both tika and gate. However, the most important bit is to specify how the document text is passed from Tika processor to GATE document processor:

## TIKA CONFIGURATION ## #... tika.binaryFieldName = encounter_binary_doc tika.tikaFieldName = tika_output #... ##### GATE CONFIGURATION ## # ... gate.fieldsToGate = tika_output gate.gateFieldName = gate # ...

Tika item processor will extract the text from the document initially stored in binary form in encounter_binary_doc field (property: tika.binaryFieldName ; see Example 4 for the DB schema). Then, it will store the extracted text in a tika_output field (property: tika.tikaFieldName) in the Document model. The GATE application will then read the text from tika_output field (property: gate.fieldsToGate), process it and store the extracted annotations in gate field (property: gate.gateFieldName) in the Document model. At the end of processing of the record, a resulting JSON with all the available fields will be generated and send to ElasticSearch.

Deployment information

When running setup.sh script, a number of separate directories will be created. Since NLP components for pipelines require additional applications to be installed in the system, CogStack provides another, extended image containing them. In this example, hence cogstacksystems/cogstack-pipeline-gate:latest image is being used.

Apart from that, this example uses the standard stack of microservices and also uses a single CogStack properties file, running only one instance of CogStack data processing engine.

Example 10

General information

This example covers a use-case of running a pipeline of multiple components combined together:

• we use the pipeline to quickly ingest documents from the database into an elastic search index

• in parallel, we use a separate pipeline service to get documents from ES, send them to MedCAT service

• annotate the free-text documents with MedCAT

• retreive the annotated text and reingest it into a separate index in ES

Deployment information

To get this working please do the following steps:

1. Go to ./data/models/ and execute the download_medmen.sh (you have to be inside the folder and execute the script !) using the command: bash download_medmen.sh

2. Go to cogstack-pipeline/ and start the docker container using the following command : docker-compose up -d

Please be patient, this example will take time until it finishes ingesting all the documents, afterwards, proceed with the following

1. If all goes well, navigate to http://localhost:5601/ and login using the following credentials :
   -username: elastic
   -password: admin

2. Navigate to http://localhost:5601/app/management/kibana/indexPatterns/create

3. Click on create index pattern:

   • input sample_observations_view into the input field.
     Click on next step

   • select observation_timestamp from the dropdown.
     Click Next

   • Done, you should have the sample text index ready to view !

4. We need to redo the instructions from step 3
   Click on create index pattern:

   • paste sample_observations_view_annotations into the input field.
     Click on next step

   • select meta.observation_timestamp from the dropdown.
     Click Next

   • Done, you should have the sample text index ready to view !

5. All Done !
   You can view how many records you have and how much space your index takes + other stats at http://localhost:5601/app/management/data/index_management/indices

   To view the records inserted, go to : http://localhost:5601/app/discover , change the query timeframe to last 2-3 years and perform a search to see some results.

The data used in this example is taken from example2...