GeoMesa NiFi provides several processors:
||Ingest data into a GeoMesa Accumulo datastore|
||Ingest data into a GeoMesa HBase datastore|
||Ingest data into a GeoMesa File System datastore|
||Ingest data into a GeoMesa Kafka datastore|
||Ingest data into a GeoMesa Redis datastore|
||Ingest data into an arbitrary GeoTools datastore|
||Read GeoMesa Kafka messages and output them as NiFi records|
||Use a GeoMesa converter to create files in a variety of geometry-enabled formats|
12.2.1. Records, Converters, and Avro¶
Put NiFi processors come in three different flavors. They all write to the same data stores, but
they vary in how the input data is converted into GeoTools
SimpleFeatures (which are necessary for ingest).
The standard processors use the GeoMesa Convert framework to define
SimpleFeatureTypes and the mapping from
input files to
SimpleFeatures. Converters can be re-used in the GeoMesa command-line tools and other non-NiFi
projects. See Converter Processors for details.
The record-based processors use the NiFi records API to define the input schema using a NiFi
SimpleFeatureTypes can be managed in a centralized schema registry. Similarly, records
can be manipulated using standard NiFi processors before being passed to the GeoMesa processor. The use of standard
NiFi APIs greatly reduces the amount of GeoMesa-specific configuration required. See Record Processors
AvroToPut processors will ingest GeoMesa-specific GeoAvro files without any configuration. GeoAvro
is a special Avro file that has
SimpleFeatureType metadata included. It can be produced using the GeoMesa
command-line tools export in
avro format, the
ConvertToGeoFile processor, the
GeoAvroRecordSetWriterFactory record writer factory, or directly through an instance of
org.locationtech.geomesa.features.avro.AvroDataFileWriter. GeoAvro is particularly useful because it is
self-describing. See Avro Processors for details.
12.2.2. Common Configuration¶
All types of input processors have some common configuration parameters for controlling data store writes:
||Additional resources to add to the classpath, e.g. converter definitions|
||Use an appending writer (for new features) or a modifying writer (to update existing features)|
||When using a modifying writer, the attribute used to uniquely identify the feature. If not specified, will use the feature ID|
Controls how differences between the configured schema and the existing schema in the data store (if any) will be handled.
||The number of flow files that will be processed in a single batch|
||Enable caching of feature writers between flow files, useful if flow files have a small number of records (see below)|
||How often feature writers will be flushed to the data store, if caching is enabled|
126.96.36.199. Feature Writer Caching¶
Feature writer caching can be used to improve the throughput of processing many small flow files. Instead of a new feature writer being created for each flow file, writers are cached and re-used between operations. If a writer is idle for the configured timeout, then it will be flushed to the data store and closed.
Note that if feature writer caching is enabled, features that are processed may not show up in the data store immediately. In addition, any features that have been processed but not flushed may be lost if NiFi shuts down unexpectedly. To ensure data is properly flushed, stop the processor before shutting down NiFi.
Alternatively, NiFi’s built-in
MergeContent processor can be used to batch up small files.