Managing data imports from datasources

This section describes the Data import app, which allows you to manage import jobs from JDBC datasources.

If you are ready to import data from a datasource, go to the Data model app and follow the instructions in Importing data from a datasource.

To import data from Excel or CSV files, go to the Data model app and follow the instructions in Importing data from a spreadsheet.

Datasource reflection jobs

Each datasource reflection job is defined by a configuration. Configurations can be managed on the Datasource Reflection Jobs screen:

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Each job can be either scheduled to run periodically by using a cron syntax or can be run manually from the Jobs screen. The following actions can be performed on the Jobs screen:

  • Add a new job

  • Edit an existing job

  • Manually run a job

  • Delete a job configuration

  • Clone a job configuration

  • Enable or disable a scheduled job

Defining a new datasource reflection job

To define a new datasource reflection job, complete the following steps:

Step 1: Data input

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Give a name and a description (optional) to the job, then select a datasource and write the query.

If you are selecting a virtual index as a datasource, then start with the default query for that virtual index.

Writing a query

The query defined in the reflection configuration is written in the datasource language.

The query can be written using mustache and the following variables are provided, if applicable, when converting the query to a string:

max_primary_key: the maximum value of the primary key in Elasticsearch, available if the primary key is numeric.

last_record_timestamp: the UTC timestamp at which the last record was successfully processed by an reflection job.

last_record: an array with the scalar values in the last record that was successfully processed by the reflection job.

Example:

SELECT * FROM "Player" {{#max_primary_key}}
WHERE "NAME">'{{max_primary_key}}'{{/max_primary_key}}

Step 2: Set the transformation pipeline (optional)

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This is an optional step where a pipeline may be defined to enrich the reflected documents. For more information, see Datasource reflection pipelines.

Step 3: Mapping

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The mapping for the fields received from the datasource is auto-detected. However, you can change the mapping to other valid field types and you can add new fields if any fields were not auto-detected.

Advanced Mapping

This switch gives advanced users the option to add a JSON object to define the mapping in Elasticsearch format. For example, selecting Date and switching the Advanced Mapping switch to ‘on’ would allow you to define one or more parsing formats for the date field, such as {"format":"yyyy-MM-dd HH:mm"}. These formats must match the actual structure of the date in the source documents, otherwise the import will not complete. For more information, see Date formats in fields.

Step 4: Define the target index

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Specify the name of the Elasticsearch index to be used for reflection and configure the preferences, such as the primary key, indexing options, and custom credentials.

Step 5: Schedule the job

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Allow the job to be scheduled. The scheduler accepts cron syntax to schedule jobs.

Datasource reflection pipelines

Pipelines may be used to enrich documents before they are indexed to Elasticsearch.

Siren Platform provides a JSON web service processor pipeline. Elasticsearch ingest processors can be also be used.

For more information about transform pipelines, see Sample transform pipelines.

JSON Web service processor:

The following pipeline can be used to call an external JSON Web service and copy the returned JSON structure to the record.

A transform that relies on external Web services to enrich the documents might be slow to respond or might result in the import process stalling. If you notice such an issue, then go to Management → Advanced Settings and reduce the value of the ingest-xlsx:bulk_package_size parameter.

{
  "description": "enriching documents from a web service",
  "processors": [
    {
      "json-ws": {
        "resource_name": "siren-nlp",
        "method": "post",
        "url": "http://35.189.96.185/bio",
        "input_map": {
          "$.Abstract": "text"
        },
        "output_map": {
          "Abstract_text_mined_entities": "$"
        },
        "input_default": {
          "text": "''"
        }
      }
    }
  ]
}

In this example, the configuration takes the value of the Abstract field (path syntax) and posts a request {"text": abstract_value} to http://35.189.96.185/bio. If the Abstract field is null, an empty string will be sent instead (input_default).

The JSON response object is used as the value of a new field Abstract_text_mined_entities at the top level ($) of the document.

This pipeline is defined with a json-ws field inside processors.

The following configurations are also available:

Table 1. json-ws
Name Required Default Description

method

no

get

The HTTP method: get, post, put

url

yes

-

The URL endpoint of the web service.

requests_per_second

no

0

The expected maximum number of requests per second.

resource_name

yes

-

Apply a name to the web service resource. Every processor instance with the same resource name are consolidated and submitted to the requests_per_second limit.

input_map

yes

-

A map with a JSON Path expression as the key and a field name as the value. It builds the JSON structure that will be submitted to the external web service.

input_default

no

-

A map with a field as the key and a default value. For a given field, this map provides a default value if the JSON Path expression of the input_map does not return any value.

output_map

yes

-

A map with a field name as the key and a JSON Path expression as the value. The JSON Path expressions are applied to the JSON structure returned by the external web service. The indexed document is filled with results of the JSON Path expressions associated to the given field name. Already existing content for the field name is replaced.

output_default

no

-

A map with a field as the key and a default value. For a given field, this map provides a default value if the JSON Path expression of the output_map does not return any value.

error_output_field

no

-

If this field is not blank and an error occurs while calling the external external the field is filled with a the error message and the ingestion process is not stopped. If the field is empty, an exception is thrown.

time_out

no

300

This timeout determines how many seconds should the request wait for a response before failing the request.

username

no

-

If a username is provided the HTTP(S) connection to the external web service will use it as the username for an HTTP basic authentication.

password

no

-

If a password is provided the HTTP(S) connection to the external web service will use it as the password for an HTTP basic authentication.

Integrating Neo4j data

Neo4j is a graph database management system, which uses graph structures with nodes, relations, and properties to represent and store data. Siren can now ingest and reflect (periodically update) Neo4j data. The Neo4j Import Wizard (beta) makes this a straightforward process.

  • Nodes represent entities that are to be tracked, and correspond to a record in a relational database.

  • Relations are lines that connect nodes to other nodes, representing the relationship between them. Relations are the key concept in graph databases because they represent an abstraction that is not directly implemented in a conventional relational database.Properties describe information relevant to nodes.

  • Properties describe information relevant to nodes.

The following diagram shows two nodes with five relations between them. One of the nodes also has a relation with itself (node properties are not shown here).

image

Using the Neo4j Import Wizard (beta)

The Neo4j wizard has been deprecated and will be removed in the next major release.

The Neo4j Import Wizard (beta) makes it easy to import Neo4j data from a datasource and then configure and view the data model.

  1. Go to Management > Data Sources.

  2. In the New Datasource Type dropdown menu, click JDBC.

  3. Specify the details for the Neo4j as the datasource, then click Save.

  4. Go to Data Import app and click Reflection Jobs; add a new job.

  5. Select Neo4j as the datasource; you then get an option to use the Neo4j Importer.

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  6. Click Use Neo4j Importer. The Neo4j Import Wizard screen opens.

  7. Select the required nodes from the Select Nodes dropdown menu, and click Next.

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    All data reflection jobs for nodes and relations are displayed. Note that relations are also listed under Node Name. This is because Neo4j relations contain data and Siren Investigate runs a reflection job for each relation, just like a node.

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    Ensure that an appropriate primary key is selected. The use of stable unique identifiers as primary keys in your Neo4j data model is recommended.
  8. Click Confirm. Wait for the data to start reflecting, which means that all jobs have indexed at least one document, indicating that the fields are mapping successfully. The Continue button becomes enabled at this point.

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    While a job is indexing, its status is shown as running; when a job completes, the status changes to successful.

    You can see the document count and other information by hovering over the status.

    image

  9. If reflection jobs are failing, close the wizard go back to fix them. You can continue later by following the notification in the Datasource Reflection Jobs page and clicking Pending Jobs.

    image

  10. Click Continue Neo4j Job.

    image

    Pending jobs are stored in server cache, which will be wiped out if the Siren Investigate server is restarted or shut down. Its behavior is unpredictable when multiple Investigate instances are running behind a load balancer.

  11. On the Configure Saved Searches screen, you can modify the search name, and specify a color for the nodes and relations:

    image

  12. Click Next.

  13. On the Configure Relations screen, you can modify the Relation Label and the Inverse Relation Label for each relation:

    image

  14. Click Create Ontology.

  15. You can now go to the Data Model page to view the Neo4j data model:

    image

Node behavior

You should note the following aspects of node behavior:

  • If a node has labels Location and Residence, then it will be reflected onto both indices.

  • A field node_labels is added to every document to denote all its labels.

  • Extra fields are added to all nodes, including node_id (containing the value of the primary key). Relation nodes have a few additional fields: start_node_id, end_node_id, relation_type.

  • In the case of relation-based reflection jobs, relation documents are only included for added nodes.

Other considerations

  • The same field mappings should not conflict for a single node.

  • All id()s are unique. Note that the use of stable unique identifiers in your Neo4j data model as primary keys is recommended.

    Neo4j does provide a unique id for each node and relationship, but they are not persistent. The id can be accessed by returning id(node) or id(relationship). While this id is unique, it can change if the database store is compacted.

Adding a Shortest Path script for Neo4j

The following steps add a 'Shortest Path' query script to the Graph Browser to find the shortest path between two or more (Neo4j reflected) data nodes.

Use the Neo4j Import Wizard (beta) to import your Neo4j data.

Locate the Shortest Path script in the demo data supplied with Siren 10.3.1; alternatively, you can find the script at this location.

  1. Navigate to Management > Scripts ① and create a new script.

  2. Enter a title and description for the script ②.

  3. Select contextual from the Type drop-down list ③.

  4. Paste the script into the Source section ④.

  5. Click Save ⑤.

    image

  6. Go to the Graph Browser and click the Edit button on the top panel.

  7. On the Options tab on the left, click the Add Contextual Script button ① under the Contextual Function.

  8. Select the newly created script from the drop-down list ② to add it to the Graph Browser.

  9. Click Save ③.

    image

Running the Shortest Path script

You can now use this script to compute shortest paths between your selected (Neo4j reflected) data nodes.

Add the Neo4j reflected nodes to the Graph Browser.

  1. Select the required nodes ①.

  2. Right-click and select Neo4j Shortest Path (the name of the script) ②.

    image

  3. In the dialog that opens, enter the maximum path length and click OK (both of the fields will be already populated).

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    The shortest path between the nodes is displayed.

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Neo4j Shortest Path limitations

The following limitations apply to running a Neo4j Shortest Path script:

  • The Neo4j Import Wizard must have been used for importing the data.

  • All selected nodes should belong to the same datasource (Neo4j cluster).

  • Neo4j field names have not been changed.

  • Neo4j reflection target index schema ${datasourceId}-${nodeType} has not been changed.

  • All documents for a single node type/relation type must be indexed to a single index each.

Security setup

See the the access control permissions (ACL) required by this data import plugin for Elasticsearch:

ingestion_role:
cluster:
  - 'cluster:admin/federate/connector/ingestion/search' // To fetch the list of ingestion configs
  - 'cluster:admin/federate/connector/ingestion/run' // To manually trigger an ingestion
  - 'cluster:admin/federate/connector/jobs/abort' // To abort a job
  - 'cluster:admin/federate/connector/ingestion/get' // To fetch an ingestion config.
  - 'cluster:admin/federate/connector/ingestion/put' // To create an ingestion config
  - 'cluster:admin/federate/connector/ingestion/delete' // To delete an ingestion config
  - 'cluster:admin/federate/connector/datasource/sample' // To sample a SQL query
  - 'cluster:admin/ingest/pipeline/simulate' // To test transform pipeline
  - 'cluster:admin/ingest/pipeline/put' // To put transform pipeline (Excel)
  - 'cluster:admin/ingest/pipeline/delete' // To delete a transform pipeline (Excel) (We need to clear temporary pipelines after import)
indices:
  'csv-*': //This can be limited to specific indices and such permissions would be regarded by Excel Import
    '*':
      - 'indices:admin/get' // To check if an index already exists, if received 403 then user cannot use excel import on that index
      - 'indices:admin/create' // Create an index (You may prevent it in case you want users to only append data to an existing index)
      - 'indices:admin/delete' // Delete an index (You may prevent it, in case you don't want users deleting stuff)
      - 'indices:admin/mapping/put' // Define mapping (You may prevent it in case you want users to only append to an existing index and modify mappings)
      - 'indices:data/write/index' // (To write docs)
      - 'indices:data/write/bulk[s]' // (To write docs)
      - READ                         # To see field capabilities on data model page
      - VIEW_INDEX_METADATA          # Needed to create index patterns
  '?siren-excel-configs':
      '*':
        - 'indices:data/read/search'   # List saved configs
        - 'indices:data/write/index'   # Create a saved config
        - 'indices:data/write/bulk[s]' # Create a saved config
        - 'indices:data/read/get'      # Use a saved config
        - 'indices:data/write/delete'  # Delete a saved config

Scheduler Cron Syntax

Introduction

Cron-Expressions are used to configure schedules. Cron-Expressions are strings that are actually made up of seven sub-expressions, that describe individual details of the schedule. These sub-expression are separated with white-space, and represent:

Seconds
Minutes
Hours
Day-of-Month
Month
Day-of-Week

Individual sub-expressions can contain ranges and/or lists.

Wild-cards (the ‘‘ character) can be used to say “every” possible value of this field. Therefore the ‘‘ character in the “Month” field of the previous example simply means “every month”. A ‘*’ in the Day-Of-Week field would therefore obviously mean “every day of the week”.

All of the fields have a set of valid values that can be specified. These values should be fairly obvious – such as the numbers 0 to 59 for seconds and minutes, and the values 0 to 23 for hours. Day-of-Month can be any value 1-31, but you need to be careful about how many days are in a given month! Months can be specified as values between 0 and 11. Days-of-Week can be specified as values between 1 and 7 (1 = Sunday).

The ‘/’ character can be used to specify increments to values. For example, if you put ‘0/15’ in the Minutes field, it means ‘every 15th minute of the hour, starting at minute zero’. If you used ‘3/20’ in the Minutes field, it would mean ‘every 20th minute of the hour, starting at minute three’ – or in other words it is the same as specifying ‘3,23,43’ in the Minutes field. Note the subtlety that “/35″ does *not mean “every 35 minutes” – it mean “every 35th minute of the hour, starting at minute zero” – or in other words the same as specifying ‘0,35’.

The ‘?’ character is allowed for the day-of-month and day-of-week fields. It is used to specify “no specific value”. This is useful when you need to specify something in one of the two fields, but not the other.

Example Cron Expressions

Example 1 – an expression to create a trigger that simply fires every 5 minutes

0 0/5 * * * ?

Example 2 – an expression to create a trigger that fires every 5 minutes, at 10 seconds after the minute (i.e. 10:00:10 am, 10:05:10 am, etc.).

10 0/5 * * * ?

Example 3 – At 10 seconds of every even minute

10 */2 * ? * *

Example 4 – At 10 seconds of every odd minute

10 1-59/2 * ? * *

Example 5 – Every 5 minutes, Weekdays from 8-5.

0 */5 8-16 ? * 2-6

Format

Field Mandatory Allowed Values Allowed Special Characters

Seconds

Yes

0-59

, – * /

Minutes

Yes

0-59

, – * /

Hours

Yes

0-23

, – * /

Day of the Month

Yes

1-31

, – * ? / L W

Month

Yes

1-12 or JAN-DEC

, – * /

Day of week

Yes

1-7 or SUN-SAT

, – * ? / L #

Year

No

empty, 1970-2099

, – * /