19.11. FileSystem Data Store Example¶

In this simple example we will ingest a small CSV into a local filesystem data store partitioned by an daily,z2-2bit scheme. To begin, start by untaring the geomesa-fs distribution. Inside this distribution you will find an examples folder which contains an example csv file that we will ingest. First set the version you want to use:

$ export TAG="5.1.0"
$ export VERSION="2.12-${TAG}" # note: 2.12 is the Scala build version

Then download and extract the binary distribution:

# download and unpackage the most recent distribution:
$ wget "https://github.com/locationtech/geomesa/releases/download/geomesa-${TAG}/geomesa-fs_${VERSION}-bin.tar.gz"
$ tar xvf geomesa-fs_${VERSION}-bin.tar.gz
$ cd geomesa-fs_${VERSION}
$ cat examples/csv/example.csv

The output should look like:

ID,Name,Age,LastSeen,Friends,Lon,Lat,Vis
23623,Harry,20,2015-05-06,"Will, Mark, Suzan","patronus->10,expelliarmus->9",-100.236523,23,user
26236,Hermione,25,2015-06-07,"Edward, Bill, Harry","accio->10",40.232,-53.2356,user
3233,Severus,30,2015-10-23,"Tom, Riddle, Voldemort","potions->10",3,-62.23,user&admin

As you can see, there are 3 records in the file. GeoMesa ships with a pre-installed SimpleFeatureType and converter for this example file which can be found in the conf/application.conf file. Running bin/geomesa-fs env will show that there is an example-csv type and converter installed along with many other types including twitter, gdelt, osm, etc:

$ bin/geomesa-fs env

The output should look like:

Simple Feature Types:
example-avro
example-csv
example-csv-complex
...

Simple Feature Type Converters:
example-avro-header
example-avro-no-header
example-csv
...

For this example we’ll ingest the three rows to a local filesystem. Note that the records are all on different days:

2015-05-06
2015-06-07
2015-10-23

and that the geometries fall into two quadrants of the world. The first record is in the upper left quadrant and the second and third records are in the lower right quadrant:

Lon          Lat
-100.236523  23
40.232      -53.2356
3           -62.23

Now lets ingest.

$ bin/geomesa-fs ingest -p /tmp/dstest -e parquet -s example-csv -C example-csv \
--partition-scheme daily,z2-2bit examples/csv/example.csv

The output should look like:

INFO  Creating schema example-csv
INFO  Running ingestion in local mode
INFO  Ingesting 1 file with 1 thread
[============================================================] 100% complete 3 ingested 0 failed in 00:00:01
INFO  Local ingestion complete in 00:00:01
INFO  Ingested 3 features with no failures.

We can verify our ingest by running an export:

$ bin/geomesa-fs export -p /tmp/dstest -f example-csv

The output should look like:

id,fid:Integer:index=false,name:String:index=true,age:Integer:index=false,lastseen:Date:default=true:index=false,*geom:Point:srid=4326
26236,26236,Hermione,25,2015-06-07T00:00:00.000Z,POINT (40.232 -53.2356)
3233,3233,Severus,30,2015-10-23T00:00:00.000Z,POINT (3 -62.23)
23623,23623,Harry,20,2015-05-06T00:00:00.000Z,POINT (-100.236523 23)
INFO  Feature export complete to standard out in 1676ms for 3 features

Now lets inspect the filesystem:

$ find /tmp/dstest | sort

The output should look like:

/tmp/dstest/
/tmp/dstest/example-csv
/tmp/dstest/example-csv/2015
/tmp/dstest/example-csv/2015/05
/tmp/dstest/example-csv/2015/05/06
/tmp/dstest/example-csv/2015/05/06/2_Wcec6a2ec594a4a2eb7c7980a1baf4ab3.parquet
/tmp/dstest/example-csv/2015/05/06/.2_Wcec6a2ec594a4a2eb7c7980a1baf4ab3.parquet.crc
/tmp/dstest/example-csv/2015/06
/tmp/dstest/example-csv/2015/06/07
/tmp/dstest/example-csv/2015/06/07/1_Wcc082b9cf9bc4965b4cbf64741fee5b6.parquet
/tmp/dstest/example-csv/2015/06/07/.1_Wcc082b9cf9bc4965b4cbf64741fee5b6.parquet.crc
/tmp/dstest/example-csv/2015/10
/tmp/dstest/example-csv/2015/10/23
/tmp/dstest/example-csv/2015/10/23/1_W741f2151a4ed4eec97461a174a8588b7.parquet
/tmp/dstest/example-csv/2015/10/23/.1_W741f2151a4ed4eec97461a174a8588b7.parquet.crc
/tmp/dstest/example-csv/metadata
/tmp/dstest/example-csv/metadata/storage.json
/tmp/dstest/example-csv/metadata/.storage.json.crc
/tmp/dstest/example-csv/metadata/update-2015-05-06-2-12240906-4171-4ab0-acfe-d2ce9c5fff76.json
/tmp/dstest/example-csv/metadata/.update-2015-05-06-2-12240906-4171-4ab0-acfe-d2ce9c5fff76.json.crc
/tmp/dstest/example-csv/metadata/update-2015-06-07-1-ecd68700-88e3-4f04-9438-84b6ab935907.json
/tmp/dstest/example-csv/metadata/.update-2015-06-07-1-ecd68700-88e3-4f04-9438-84b6ab935907.json.crc
/tmp/dstest/example-csv/metadata/update-2015-10-23-1-667f27a7-4f64-472a-80ed-82e8f1e65575.json
/tmp/dstest/example-csv/metadata/.update-2015-10-23-1-667f27a7-4f64-472a-80ed-82e8f1e65575.json.crc

Notice that we have a directory structure laid out based on our daily,z2-2bit scheme. Notice the first parquet file path is composed of a date path 2016/05/06 and then a z2 ordinate of 2, which is part of the file name

/tmp/dstest/example-csv/2015/05/06/2/2_Wcec6a2ec594a4a2eb7c7980a1baf4ab3.parquet

The rest of the parquet file name is a UUID, which allows for multiple threads to write different files at once without interference. If we ingested additional data, another file would be created under the partition, and GeoMesa would scan them both at query time.

Each new file (or file deletion) will create a separate metadata file, which contains details on the file:

$ cat /tmp/dstest/example-csv/metadata/update-2015-05-06-629788a4-6a70-4009-ae20-c45602a88483.json

The output should look like:

{
    "action" : "Add",
    "count" : 1,
    "envelope" : {
        "xmax" : -100.2365,
        "xmin" : -100.2365,
        "ymax" : 23.0,
        "ymin" : 23.0
    },
    "files" : [
        "2_Wcec6a2ec594a4a2eb7c7980a1baf4ab3.parquet"
    ],
    "name" : "2015/05/06/2",
    "timestamp" : 1538148168948
}