9.2. Parsing and Validation

GeoMesa provides options for dealing with bad input records which can be configured on your converter. For example, you may want to skip over bad records for some input sources or fail entire files in other cases. The Converter framework provides a few options that can help control how errors in input data are handled.

9.2.1. Validators

At their core, converters transform input streams into SimpleFeatures. Validators provide hooks to validate properties of those SimpleFeatures before they are written to GeoMesa. For example, you may want to validate that there is a geometry field and that the geometry is valid.

There are four validators provided for use with GeoMesa converters:

• index - validates that the SimpleFeature has a geometry and date that are within the space/time bounds of the relevant GeoMesa Z-Index implementations (i.e. Z2, Z3, XZ2, XZ3)
• has-geo - validates that the SimpleFeature has a non-null geometry
• has-dtg - validates that the SimpleFeature has a non-null date
• cql - validates that the SimpleFeature passes an arbitrary CQL filter

Additional validators may be loaded through Java SPI by by implementing org.locationtech.geomesa.convert2.validators.SimpleFeatureValidatorFactory and including a special service descriptor file. See below for additional information.

By default the index validator is enabled. This is suitable for most use cases, as it will choose the appropriate validator based on the SimpleFeatureType. To enable other validators, specify them in the options block of your converter definition:

geomesa.converters.myconverter {
  options {
    validators = [ "has-dtg", "cql(bbox(geom,-75,-90,-45,90))" ]
  }
}

Validation can be disabled by setting it to an empty array.

9.2.1.1. Custom Validators

Custom validators may be loaded through Java SPI by by implementing org.locationtech.geomesa.convert2.validators.SimpleFeatureValidatorFactory, shown below. Note that validators must be registered through a special service descriptor file.

trait SimpleFeatureValidatorFactory {

  /**
    * Well-known name of this validator, for specifying the validator to use
    *
    * @return
    */
  def name: String

  /**
    * Create a validator for the given feature typ
    *
    * @param sft simple feature type
    * @param metrics metrics registry for reporting validation
    * @param config optional configuration string
    */
  def apply(sft: SimpleFeatureType, metrics: ConverterMetrics, config: Option[String]): SimpleFeatureValidator
}

Valdiators are provided with a Dropwizard MetricRegistry, which can be used to register custom validation metrics. See Metrics, below.

When specifying validators in a converter config, the name of the factory must match the validators string. Any additional arguments may be specified in parentheses, which will be passed to the validator method. For example:

geomesa.converters.myconverter {
  options {
    validators = [ "my-custom-validator(optionA,optionB)" ]
  }
}

import org.locationtech.geomesa.convert2.validators.SimpleFeatureValidatorFactory

class MyCustomValidator extends SimpleFeatureValidatorFactory {

  override val name: String = "my-custom-validator"

  override def apply(
      sft: SimpleFeatureType,
      metrics: ConverterMetrics,
      config: Option[String]): SimpleFeatureValidator = {
    if (config.exists(_.contains("optionA"))) {
      // handle option a
    } else {
      // handle other options
    }
  }
}

See the GeoMesa unit tests for a sample implementation.

For more details on implementing a service provider, see the Oracle Javadoc.

9.2.2. Error Mode

There are two types of modes for handling errors:

• skip-bad-records
• raise-errors

raise-errors mode will throw an IOException if bad data is detected based on parsing or validation. This can be especially useful when first developing and testing a converter definition. skip-bad-records mode will still provide debug level logging but will not throw an exception. To configure the error mode add the following option to your converter’s typesafe config:

geomesa.converters.myconverter {
  options {
    error-mode = "raise-errors"
  }
}

9.2.3. Parse Mode

The parse mode option allows you to control whether a file is parsed incrementally or fully before being converted. This is important when it comes to validation. There are two modes available:

• incremental
• batch

Since converters provide iterators of SimpleFeatures, the default parse mode is incremental which provides better performance and less memory overhead. Using incremental parse mode means that data may be partially ingested into GeoMesa before an error is raised. In most cases this is appropriate and can be handled by the client code.

Using batch mode will buffer an entire input stream or file in memory to validate the data which is less performant and requires more memory. batch mode does, however, prevent partially ingested data streams. It is unlikely that you need to use batch mode in environments where data is properly sanitized.

To configure the parse mode use add an option to your converter’s typesafe config:

geomesa.converters.myconverter {
  options {
    parse-mode = "incremental"
  }
}

9.2.4. Logging

To view validation logs you can enable info or debug level logging on the packages org.locationtech.geomesa.convert and org.locationtech.geomesa.convert2.

When logging is enabled at the info level, it will just show the field that failed. When enabled at the debug level, it will show the entire record, along with the stack trace.

9.2.5. Metrics

Converters use the Dropwizard Metrics library to register metrics on successful conversions, failed conversions, validation errors, and processing rates. Metrics can be accessed through the converter evaluation context, or can be exposed through reporters configured in the converter options:

geomesa.converters.myconverter {
  options {
    reporters = [
      {
        type           = "slf4j"
        logger         = "com.example.MyConverter"
        level          = "INFO"
        rate-units     = "SECONDS"
        duration-units = "MILLISECONDS"
        interval       = "10 seconds"
      }
    ]
  }
}

Reporters are available for SLF4J, CloudWatch, Graphite, and Ganglia. To use CloudWatch, Graphite, or Ganglia, you must include a dependency on geomesa-convert-metrics-cloudwatch_2.11, geomesa-convert-metrics-graphite_2.11, or geomesa-convert-metrics-ganglia_2.11, respectively. The CloudWatch module uses the default credentials and region specified in your AWS profile config. Note that using Ganglia requires an additional GPL-licensed dependency info.ganglia.gmetric4j:gmetric4j, which is excluded by default. The reporters can be configured as follows:

geomesa.converters.myconverter {
  options {
    reporters = [
      {
        type        = "cloudwatch"
        units       = "MILLISECONDS"
        interval    = "60 seconds"
        namespace   = "geomesa"
        raw-counts  = false
        zero-values = true
      },
      {
        type           = "graphite"
        url            = "localhost:9000"
        prefix         = "example"
        rate-units     = "SECONDS"
        duration-units = "MILLISECONDS"
        interval       = "10 seconds"
      },
      {
        type            = "ganglia"
        group           = "example"
        port            = 8649
        addressing-mode = "multicast" // or unicast
        ttl             = 32
        ganglia311      = true
        rate-units      = "SECONDS"
        duration-units  = "MILLISECONDS"
        interval        = "10 seconds"
      }
    ]
  }
}

Additional reporters can be added at runtime by implementing org.locationtech.geomesa.convert2.metrics.ReporterFactory and registering the new class as a service provider.

9.2.6. Transactional Considerations

Most of the datastores that GeoMesa works with (Accumulo, HBase, etc) do not provide transactions. Therefore, streaming data in and out of a converter and into an ingest pipeline is not transactional. To mimic transactions you can use a batch parse mode with raise-errors error mode and with the index validator. Note that this may increase your memory requirements and hurt performance:

geomesa.converters.myconverter {
  options {
    validators = [ "index" ]
    parse-mode = "batch"
    error-mode = "raise-errors"
  }
}

If you need notification of bad input data you may consider using an error mode of raise-errors with an incremental parse mode:

geomesa.converters.myconverter {
  options {
    validators = [ "index" ]
    parse-mode = "incremental"
    error-mode = "raise-errors"
  }
}

If you are using a framework such as the GeoMesa Nifi processor, then the file will still be routed to an error relationship but you may experience partially ingested data. See GeoMesa NiFi Bundle for more info.

9.2.7. Managing Parsing and Validation Configuration with System Properties

For inferred converters, one can manage the parsing, line, and validation modes via system property or geomesa-site.xml. For each of the modes in the table below, the corresponding property name is given.

Mode	System Property
Error Mode	geomesa.converter.error.mode.default
Parse Mode	geomesa.converter.parse.mode.default
Line Mode	geomesa.converter.line.mode.default
Validator Mode	geomesa.converter.validators