## Basic Usage#

The purpose of the Data Loader tool is to extract data from different sources, including the following: supported databases, CSV and Excel files (xls and xlsx) and then upload them into Squirro.

The Data Loader is called from the command line with multiple arguments, some of which are mandatory.

## Arguments#

The following table lists all the arguments:

 Argument Mandatory Description General Options –help, -h Show a help message and exit. –version Output the tool version and exit. –verbose, -v Increase log verbosity. Not specified: the tool outputs all warnings and errors. Specified once or more: informational messages are also output. Specified twice or more: debugging messages are shown. Specified three times or more: in addition to the debug logging object calls are also shown. –log-file Path to a log file on disk, where the log output is to be stored. If this is not specified, the log messages are shown on the console. –parallel-uploaders NUMBER Number of uploaders (default is 1). Parallel uploading is currently unsupported in the data loader on Microsoft Windows. –meta-db-dir PATH Directory of the SQLite metadata database. –meta-db-file STRING File name of the SQLite metadata database. –transform-items Apply item transformation on the client/local machine instead of on the server. This flag must be used when loading data to Squirro servers with version <= 3.3.0, or optionally, when it is preferred to perform the item transformation on the client/local machine. From Squirro version 3.3.1, the default behaviour is that the item transformation takes place on the server, and specifically by the Transform Input step in the Pipeline Workflow. Connection Options (see Connecting to Squirro for finding these values) –token TOKEN -t TOKEN Yes The Authentication Token with which to authenticate. If the token value starts with a dash, you need to use an equal sign to specify the value like this: --token="-12345…" –cluster URL -c URL The Squirro Cluster into which to import the data. –project-id PROJECT_ID Yes The Project identifier into which to import the data. Testing Options –limit LIMIT If set, then only this many items are sent to Squirro. This can be used to test the options on a small subset of the data to make sure the mapping, facets and pipelets all work correctly. –dry-run, -n Do all the processing, except server-side actions (no uploads or facet configuration). Source - Item Mapping Options –map-title STRING Which column is mapped to the “title” field. –map-abstract STRING Which column is mapped to the “summary” field. –map-created-at STRING Which column is mapped to the “created_at” field. –map-id STRING Which column is mapped to the “external_id” field. –map-body [STRING…] Which columns are mapped to the “body” field. –map-body-mime STRING Which column is mapped to the MIME type of the body. Use --body-mime to set this to a fixed value. The typical values in this field for this are either text/html or text/plain. –body-mime STRING Fixed MIME type of the body. Use --map-body-mime to map to a column instead. The typical values for this are either text/html or text/plain. –map-url STRING Which column is mapped to the “link” field. –map–webshot-url STRING Which column is mapped to the webshot_url for thumbnail extraction –map–webshot-picture-hint STRING Which column is mapped to the webshot_picture_hint. –map-file-name STRING Which column is mapped to the file-name. –map-file-mime STRING Which column is mapped to file mime type. –map-file-data STRING Which column is mapped to file contents. –map-file-compressed STRING Which column specifies if the file is compressed with gz. Possible values should be ‘y, yes, t, true, 1’, case insensitive. --map-flag- STRING Which column determines if the received record is an insert/update or delete. If the value is ‘d’ the record is deleted, otherwise is considered an insert/update –map-language STRING Which column is matched to the language. ItemUploader Options (see ItemUploader Class documentation for more information.) –object-id OBJECT_ID Object identifier. –source-id SOURCE_ID Source identifier, defaults to the input file name. –source-name SOURCE_NAME Source name, defaults to the input file name. –enable-filtering Enable item filtering to support alerts. (deprecated, use a suitable pipeline workflow) –enable-near-duplicate-detection Enable near duplicate detection to link similar documents in the Squirro user interface. (deprecated, use a suitable pipeline workflow) –batch-size NUMBER Batch size for uploads (default is auto - change value based on the size of the payload). Item pre-processing Options –body-template-file PATH Jinja2 html template file with full path. –title-template-file PATH Jinja2 html template file with full path. –abstract-template-file PATH Jinja2 html template file with full path. –pipelets-file PATH JSON file containing the pipelets called by the db loader in execution order. –pipelets-error-behavior STRING Specify job behavior in case a pipelet raises an exception. Valid values are error and warn. The default is error. –facets-file PATH JSON file containing facets configuration. Source Options –pipeline-workflow-name Select the pipeline workflow by name. Only interpreted if –pipeline-workflow-id is not set. (introduced in Squirro 2.6.0) –pipeline-workflow-id Select the pipeline workflow by ID. If not set, use default workflow. –source-type STRING Type of source to load data from. Valid values are excel, csv, database, json, filesystem, squirro, feed –source-script PATH Path of the Data Loader Plugin Python script. –source-batch-size NUMBER Batch size for source unloads (default is “1000”). –incremental-column STRING Which column will be used as incremental reference - usually for a datetime column. If missing a full load will be done. –job-id STRING Used for job locking and storing the last-known value of incremental columns. If not given, this is calculated based on the source parameters. –reset Deletes incremental date information for the current sql query. Useful to perform an incremental load with reset.

## CSV Source Options#

When using a CSV file as a data source, only full loading is supported.

Note: Data must have a header to determine the schema.

The command line parameters used for a CSV data source include the following:

 Argument Mandatory Description –csv-delimiter CHARACTER A one-character string used to separate fields. It defaults to ,. –csv-quotechar CHARACTER A one-character string used to quote fields containing special characters, such as the delimiter or quotechar, or which contain new-line characters. It defaults to ". –csv-encoding STRING A string specifying file encoding to be used, e.g. utf8. If not provided, the loader will try to best guess the encoding. –source-file PATH Yes Path of csv data file.

The following example shows a simple load from a CSV file, mapping the title, id and body of the Squirro item to columns from the sample.csv file, without using any of the additional files for facets, templating, pipelets etc.

All the rows will be loaded and the delimiter between fields is considered any , (comma) found in a row. To quote fields containing special characters the double quote character " must be used.

squirro_data_load -v \
--token $token \ --cluster$cluster \
--project-id $project_id \ --source-name csv_sample \ --source-file sample.csv \ --source-type csv \ --csv-delimiter , \ --csv-quotechar " \ --map-title Title \ --map-id ID \ --map-body Description  Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead. This example assumes that $token, $cluster, and $project_id have been declared beforehand.

## Excel Source Options#

When using an excel file as the data source, only full loading is supported and data must have a header to determine the schema.

If the first row of the data (after applying the boundaries, if needed) is not the header, a KeyError exception will be raised and the job will stop.

In this case, it’s not possible to determine the schema of the data.

The command line parameters used for an excel data source:

 Argument Mandatory Description –excel-sheet STRING Excel sheet name. Default: get first sheet. –excel-boundaries NUMBER: NUMBER Limit rows loaded from excel. Format is: start_row:rows_discarded_from_end. –source-file PATH Yes Path of excel data file.

The example below shows a simple load from an excel file, mapping only the title, id, and body of the Squirro item to columns from the sample.xlsx excel file, without using any of the additional files for facets, templating, pipelets etc.

The Data Loader tool will only load the Products sheet of the file and from this sheet the rows starting at 1 up to the last 100 rows, which will not be loaded.

squirro_data_load -v \
--token $token \ --cluster$cluster \
--project-id $project_id \ --source-name excel_sample \ --source-file sample.xlsx \ --source-type excel \ --excel-sheet Products \ --excel-boundaries 1:100 \ --map-title Title \ --map-id ID \ --map-body Description  Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead. This example assumes that $token, $cluster, and $project_id have been declared beforehand.

## JSON Source Options#

When using a JSON file as the data source, only full load is supported.

Schema of the data is determined using the first item found, therefore assuming that all items have the same structure. If that’s not the case, the loader might fail.

The command line parameters used for a JSON data source include the following:

 Argument Mandatory Description –item-schema STRING No In case the JSON objects are not available as a top-level structure, use this parameter to un-nest the JSON structure –source-file PATH No, one of –source-file or –source-folder must be provided Path of JSON data file. –source-folder PATH No, one of –source-file or –source-folder must be provided Path of directory containing multiple JSON files. Only available in CLI mode

The example below shows a load from a nested JSON file:

squirro_data_load -vv \
--cluster "$CLUSTER" \ --token "$TOKEN" \
--project-id "$PROJECT_ID" \ --source-type "json" \ --map-title "data.headline" \ --map-body "data.body" \ --map-id "data.id" \ --map-created-at "data.versionCreated" \ --source-name "JSON WIKI TEST" \ --item-schema "Items" \ --facets-file "facets.json" \ --source-file "$SOURCE_FILE" \
--transform-items


Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead.

This example assumes that $token, $cluster, and $project_id have been declared beforehand. ## Database Options# When loading from a database, both full and incremental load are supported, using a select query supplied as a string or in a file. The script uses uses SQLAlchemy to connect to any database. Tested databases: • Postgres and all databases using the postgres driver for connection (Greenplum, Redshift etc) • Microsoft SQL • Oracle • MySQL • SQLite The command line parameters used for a database source:  Argument Mandatory Description –db-connection STRING Yes Database connection string. –input-file PATH File containing the SQL code. –input-query STRING SQL query. Note that the --input-file and --input-query arguments are mutually exclusive. The following example shows a simple load from the database mapping the title, id, and body of the Squirro item to columns from a database table that is interrogated in the sample.sql file. The Data Loader tool makes a full load of all the rows specified in the sample.sql file since the argument --incremental-column is not set. squirro_data_load -v \ --token$token \
--cluster $cluster \ --project-id$project_id \
--db-connection $db_connection_string \ --source-name db_sample \ --input-file$script_dir/interaction.sql \
--source-type database \
--map-title Title \
--map-id ID \
--map-body Description


Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead.

This example assumes that $token, $cluster, and $project_id have been declared beforehand. ## Filesystem Options# The command line parameters used for a filesystem source:  Option Mandatory Description –folder PATH No, one of the –folder or –zip-file-path must be provided Filesystem location that will be indexed in Squirro –zip-file-path No, one of the –folder or –zip-file-path must be provided Absolute path to a zip file containing all the files “ “to be imported into Squirro –deletions No If set, then any files that are no longer present on the file system are also removed from Squirro. To use this, the --map-flag option also needs to be used to ensure new/updated and deleted files are handled correctly: --map-flag flag  –include-file PATH No Path to a file containing inclusion rules. This is a list of patterns that files need to be match to be indexed. If provided, then only files that match at least one pattern are indexed. –exclude-file PATH No Path to a file containing exclusion rules. This is a list of patterns for files that should not be indexed. Any file that matches at least one such pattern is not indexed, independent of whether it also matches the include rules. –skip-errors No Ignore any file system errors when processing individual files. This way a single file system read error does not prevent the entire load from succeeding. If the error is temporary, then the file will be picked up in the next load. Performance Optimisations –convert-file PATH No Path to a file containing conversion file patterns. Files that match any of these rules will be indexed with full content. See Content Extraction for the file types that Squirro supports full indexing for. By limiting to a smaller number of extensions, this allows the file system loader to only process content in Squirro for which indexing will be effective. –file-size-limit No Maximum size in megabytes of files that should be indexed with content. Also see --index-all below. –index-all No If set, then files over the --file-size-limit are indexed, but without their content. In the default case of this not being set, those files are skipped entirely. –batch-size-limit No Maximum size of requests sent to Squirro’s API for indexing of files. –deduplicate No Deduplicate files based on file content. Exact duplicates are only ever indexed ones, with duplicates ignored. Logging and Debugging –log-excludes No Log matches for inclusion/exclusion rules. –progress No Log progress verbosely. File system loading is implemented as a data loader plugin and invoked with the usual data loader. squirro_data_load -v \ --token$TOKEN \
--cluster $CLUSTER \ --project-id$PROJECT_ID \
--source-type filesystem \
--folder FOLDER_TO_INDEX \
--map-title "title" \
--map-file-name "file_name" \
--map-file-mime "file_mime" \
--map-file-data "file_data" \
--map-id "id" \
--map-created-at "created_at" \
--facets-file facets.json


Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead.

This example assumes that $token,$cluster and $project_id have been declared beforehand. ## Squirro Options# When Loading data from any squirro source following command line parameters can be used:  Argument Mandatory Description –source-cluster Yes Source Squirro Cluster URL –source-token Yes Source Squirro Token –source-project-id Yes Source Squirro Project ID –source-query No Squirro query –include-facets No If set, then keywords are included –facet-delimiter No Character or string used to delimit facets with multiple values –include-entities No If set, then entities are included –include-webshot No If set, then webshot are included only when one of –map-webshot-url or –map-webshot-picture-hint is set –progress No If set, detailed per row progress information is logged –deduplicate No If set, items will be deduplicated based on titles –retry No Number of retries to make in case of an error A simple example for loading a data from a Squirro source is given below: squirro_data_load -v \ --token$TOKEN \
--cluster $CLUSTER \ --project-id$PROJECT_ID \
--source-cluster $SOURCE_CLUSTER \ --source-token$SOURCE_TOKEN \
--source-project-id $SOURCE_PROJECT_ID \ --source-type squirro \ --source-query "*" \ --include-facets \ --include-entities \ --map-title "title" \ --map-id "id" \ --map-url "link" \ --map-created-at "created_at" \ --progress \ --deduplicate \ --retry 5  Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead. This example assumes that $token, $cluster, and $project_id have been declared beforehand.

## Feed Options#

When Loading data from any feed source following command line parameters can be used:

 Argument Mandatory Description –feed-sources Yes Space-separated list of URLs (strings). –query-timeout No Timeout (in seconds) for fetching the feed –max-backoff No Maximum number of hours to wait if the feed update frequency is low –custom-date-field No For non-standard rss datetime fields, enter the field –custom-date-format No For non-standard rss datetime formats, enter the format i.e. %m/%d/%Y. –rss-username No Username for RSS Basic Authentication –rss-password No Password for RSS Basic Authentication

A simple example for loading data from feed source is given by:

squirro_data_load -v \
--token $TOKEN \ --cluster$CLUSTER \
--project-id $PROJECT_ID \ --source-type feed \ --source-name feed_sample \ --feed-sources 'https://www.theregister.co.uk/headlines.atom' 'http://rss.nytimes.com/services/xml/rss/nyt/HomePage.xml' \ --map-title "title" \ --map-id "id" \ --map-body "description" \ --map-created-at "created_at" \ --batch-size 100 \ --source-batch-size 100 \ --facets-file facets.json  Note: The lines have been wrapped with the backslash () at the end of each line. On a bash/windows setup, you will need to use circumflex (^) instead. This example assumes that $token, $cluster, and $project_id have been declared beforehand.

## User-Defined Sources#

If data needs to be extracted from other sources than the ones described above there is the option to write a custom source.

To do this a new Python module must be created and has to implement the abstract base class.