7.13. Analytic Querying

GeoMesa provides advanced query capabilities through GeoTools query hints. You can use these hints to control various aspects of query processing, or to trigger distributed analytic processing. See Setting Query Hints for details on setting query hints.

7.13.1. Feature Sampling

Instead of returning all features for a query, GeoMesa can use statistical sampling to return a certain percentage of results. This can be useful when rendering maps, or when there are too many features to be meaningful.

Features can either be sampled absolutely, or sampled by a certain attribute. For example, given a series of points in a track, you may wish to sample by the track identifier so that no tracks are completely sampled out.

The sampling value should be a float in the range (0, 1), which represents the fractional value of features that will be returned. Due to distributed processing, the actual count returned is not guaranteed to equal the requested percentage - however, there will never be less features than requested. For example, if you sample 5 features at 10%, you will get back anywhere from 1 to 5 features, depending on how your data is distributed in the cluster.

Sampling can also be combined with the other analytic queries mentioned below.

Key Type GeoServer Conversion
QueryHints.SAMPLING Float any float
QueryHints.SAMPLE_BY String - attribute name (optional) any string
Java
Scala
GeoServer
import org.locationtech.geomesa.index.conf.QueryHints;

// returns 10% of features, threaded by 'track' attribute
query.getHints().put(QueryHints.SAMPLING(), new Float(0.1));
query.getHints().put(QueryHints.SAMPLE_BY(), "track");

import org.locationtech.geomesa.index.conf.QueryHints

// returns 10% of features, threaded by 'track' attribute
query.getHints.put(QueryHints.SAMPLING, 0.1f)
query.getHints().put(QueryHints.SAMPLE_BY, "track");

...&viewparams=SAMPLING:0.1

7.13.2. Density Queries

To populate heatmaps or other pre-rendered maps, GeoMesa can use server-side aggregation to map features to pixels. This results in much less network traffic, and subsequently much faster queries.

The result from a density query is an encoded iterator of (x, y, count), where x and y refer to the coordinates for the center of a pixel. In GeoServer, you can use the WPS DensityProcess to create a heatmap from the query result. See Heatmaps for more information.

Key Type GeoServer Conversion
QueryHints.DENSITY_BBOX ReferencedEnvelope Use WPS
QueryHints.DENSITY_GEOM String
QueryHints.DENSITY_WEIGHT String
QueryHints.DENSITY_WIDTH Integer
QueryHints.DENSITY_HEIGHT Integer
Scala
import org.geotools.data.Transaction
import org.geotools.geometry.jts.ReferencedEnvelope
import org.geotools.referencing.CRS
import org.locationtech.geomesa.index.conf.QueryHints
import org.locationtech.geomesa.index.iterators.DensityScan

val bounds = new ReferencedEnvelope(-120.0, -110.0, 45.0, 55.0, CRS.decode("EPSG:4326"))
query.getHints.put(QueryHints.DENSITY_BBOX, bounds)
query.getHints.put(QueryHints.DENSITY_WIDTH, 500)
query.getHints.put(QueryHints.DENSITY_HEIGHT, 500)

val reader = dataStore.getFeatureReader(query, Transaction.AUTO_COMMIT)
try {
  val decode = DensityScan.decodeResult(bounds, 500, 500)
  while (reader.hasNext) {
    val pts = decode(reader.next())
    while (pts.hasNext) {
      val (x, y, weight) = pts.next()
      // do something with the cell
    }
  }
} finally {
  reader.close()
}

7.13.3. Statistical Queries

GeoMesa supports generating various statistics against a data set. These stats are generated in a distributed scan, so provide built-in parallelism and require less network traffic. The following stats are supported:

  • count
  • min/max values (bounds)
  • enumeration of values
  • top-k values
  • frequency of values
  • histogram of values
  • descriptive statistics

In GeoServer you can use the StatsProcess. Otherwise, the query is controlled through the following query hints:

Key Type GeoServer Conversion
QueryHints.STATS_STRING String Use WPS
QueryHints.ENCODE_STATS Boolean (optional)
Scala
import org.geotools.data.Transaction
import org.locationtech.geomesa.index.conf.QueryHints
import org.locationtech.geomesa.index.iterators.StatsScan
import org.locationtech.geomesa.utils.stats.Stat

query.getHints.put(QueryHints.STATS_STRING, "Count()")
query.getHints.put(QueryHints.ENCODE_STATS, java.lang.Boolean.TRUE)

val reader = dataStore.getFeatureReader(query, Transaction.AUTO_COMMIT)

val result: Stat = try {
  // stats should always return exactly one result, even if there are no features in the table
  StatsScan.decodeStat(sft)(reader.next.getAttribute(0).asInstanceOf[String])
} finally {
  reader.close()
}

See Analytic Commands for information on running statistical queries through the GeoMesa command-line tools.

7.13.3.1. Explanation of Hints

7.13.3.1.1. STATS_STRING

This hint is a string describing the stats to be collected. Each type of stat has a corresponding string representation. Multiple stats can be collected at once by delimiting them with a semi-colon. Instead of constructing stat strings by hand, there are convenience methods in org.locationtech.geomesa.utils.stats.Stat that will generate valid stat strings. Stat strings can be validated by trying to parse them with org.locationtech.geomesa.utils.stats.Stat.apply. The implementing classes are contained in the package org.locationtech.geomesa.utils.stats.

Stat strings are as follows:

Type Implementation Representation
count CountStat Count()
min/max MinMax MinMax("foo")
enumeration EnumerationStat Enumeration("foo")
top-k TopK TopK("foo")
frequency Frequency Frequency("foo",<precision>)
frequency (by time period) Frequency Frequency("foo","dtg",<time period>,<precision>)
Z3 frequency Z3Frequency Z3Frequency("geom","dtg",<time period>,<precision>)
histogram Histogram Histogram("foo",<bins>,<min>,<max>)
Z3 histogram Z3Histogram Z3Histogram("geom","dtg",<time period>,<bins>)
descriptive statistics DescriptiveStats DescriptiveStats("foo","bar")
multiple stats SeqStat Count(),MinMax("foo")
grouped stats GroupBy GroupBy("foo",MinMax("bar"))

As seen in the table above, multiple stats can be calculated at once through comma delimiting. In addition, stats can be calculated on grouped values by using GroupBy on a nested stat expression.

The Z3 frequency and histogram are special stats that will operate on the Z3 value created from the geometry and date.

<time period> can be one of day, week, month, or year, and indicates how data should be grouped.

The <precision> for frequencies is defined as:

  • for geometry and Z3 types, it is the number of bits of z-index to keep (max of 64). Note that the first 2 bits do not hold any information
  • for date types, it is the number of milliseconds to group for binning
  • for number types, it is the number of digits that will be grouped together
  • for floating point types, it is the number of decimal places that will be considered
  • for string types, it is the number of characters that will be considered

The <bins> for a histogram indicate how many groupings should be made. The <min> and <max> values set the initial sizes of the groupings, but are not hard limits. The histogram will expand if needed as new values are added, but some precision may be lost.

7.13.3.1.2. ENCODE_STATS

This hint controls whether the stat will be returned as a serialized (encoded) object, or as a JSON string. Serialized stats can be deserialized using an instance of org.locationtech.geomesa.utils.stats.StatSerializer, obtained through its factory apply method.

7.13.3.2. Accessing Stats through the GeoMesa API

In addition to queries through the GeoTools API, stats can be accessed directly through the GeoMesa API. Most GeoMesa datastores implement org.locationtech.geomesa.index.stats.HasGeoMesaStats, which defines a single method:

def stats: org.locationtech.geomesa.index.stats.GeoMesaStats

In addition to running queries, the GeoMesaStats interface can be used to retrieve cached stats. See Configuring Cached Statistics for details on configuring cached stats.

7.13.4. Arrow Encoding

GeoMesa supports returning features as Apache Arrow encoded vectors. This provides an optimized columnar memory layout for fast processing and interoperability with other systems.

The result of an Arrow query will be an iterator of SimpleFeatures, where the first attribute of each will be a byte array. Concatenated together, the byte arrays will form an Arrow file, in the Arrow streaming format (i.e. no footer).

In GeoServer you can use the ArrowConversionProcess, or through WFS by setting outputFormat=application/vnd.arrow and controlling the configuration through the format_options parameter, e.g. format_options=includeFids:true;batchSize:1000. Otherwise, the encoding is controlled through the following query hints:

Key Type GeoServer Format Option
QueryHints.ARROW_ENCODE Boolean outputFormat=application/vnd.arrow
QueryHints.ARROW_INCLUDE_FID Boolean (optional) includeFids
QueryHints.ARROW_PROXY_FID Boolean (optional) proxyFids
QueryHints.ARROW_SORT_FIELD String (optional) sortField
QueryHints.ARROW_SORT_REVERSE Boolean (optional) sortReverse
QueryHints.ARROW_DICTIONARY_FIELDS String (optional) dictionaryFields
QueryHints.ARROW_BATCH_SIZE Integer (optional) batchSize
QueryHints.ARROW_FORMAT_VERSION String (optional) formatVersion

7.13.4.1. Explanation of Hints

7.13.4.1.1. ARROW_ENCODE

This hint is used to trigger an Arrow query.

7.13.4.1.2. ARROW_INCLUDE_FID

This hint controls whether to include the feature ID as an Arrow vector or not. The default is to include it.

7.13.4.1.3. ARROW_PROXY_FID

This hint controls whether to return the full feature ID, or a 4-byte proxy ID. Proxy IDs can be used for callbacks by using the proxyID() CQL filter function.

7.13.4.1.4. ARROW_SORT_FIELD

This hint allows for sorting the results by a particular attribute. Only attribute names are supported, not arbitrary CQL.

7.13.4.1.5. ARROW_SORT_REVERSE

This hint is used to flip sort order from normal (ascending) to reverse (descending).

7.13.4.1.6. ARROW_DICTIONARY_FIELDS

This hint indicates which simple feature attributes should be dictionary encoded. It should be a comma-separated list of attribute names.

7.13.4.1.7. ARROW_BATCH_SIZE

This hint will restrict the number of features included in each Arrow record batch. An Arrow file contains a series of record batches - limiting the max size of each batch can allow memory-constrained systems to operate more easily.

7.13.4.1.8. ARROW_FORMAT_VERSION

This hint controls the IPC format version for Arrow binary encoding. It should be a valid Arrow format version, i.e. 0.16 or 0.10. The Arrow IPC format changed slightly starting with version 0.15.

7.13.4.2. Example Query

Scala
import java.io.ByteArrayOutputStream
import org.geotools.data.Transaction
import org.locationtech.geomesa.index.conf.QueryHints

query.getHints.put(QueryHints.ARROW_ENCODE, java.lang.Boolean.TRUE)

val reader = dataStore.getFeatureReader(query, Transaction.AUTO_COMMIT)
val os = new ByteArrayOutputStream()

while (reader.hasNext) {
  os.write(reader.next().getAttribute(0).asInstanceOf[Array[Byte]])
}
reader.close()

// use ArrowStreamReader or other Arrow libraries to process bytes

7.13.5. Binary Encoding

GeoMesa supports returning features in a custom binary format (referred to as BIN) that uses 16 or 24 bytes per feature. This provides an extremely compact representation of a few key attributes.

The 16 byte BIN format is as follows:

<4 byte int><4 byte int><4 byte floating point><4 byte floating point>

The first integer is referred to as a track ID, and is generally used to group related points. For example, a line string may be turned into several BIN records with a common track ID. The second integer is a date represented as the number of seconds since the Java epoch (Jan. 1, 1970). The two floating point numbers represent the latitude and longitude of the record, respectively.

The 24 byte BIN format is the same as the 16 byte version, but with an additional 8 bytes at the end for arbitrary data.

The result of a BIN query will be an iterator of SimpleFeatures, where the first attribute of each will be a byte array containing one or more BIN-encoded features.

In GeoServer you can use the BinConversionProcess. Otherwise, the encoding is controlled through the following query hints:

Key Type GeoServer Conversion
QueryHints.BIN_TRACK String Use WPS
QueryHints.BIN_GEOM String (optional)
QueryHints.BIN_DTG String (optional)
QueryHints.BIN_LABEL String (optional)
QueryHints.BIN_SORT Boolean (optional)
QueryHints.BIN_BATCH_SIZE Integer (optional)

7.13.5.1. Explanation of Hints

7.13.5.1.1. BIN_TRACK

This hint is used to trigger a BIN query. It should be the name of an attribute that will be used to generate the track ID for each record.

7.13.5.1.2. BIN_GEOM

This hint controls the geometry attribute used for each record. If omitted, the default geometry of the feature type is used.

7.13.5.1.3. BIN_DTG

This hint controls the date attribute used for each record. If omitted, the default date of the feature type is used.

7.13.5.1.4. BIN_LABEL

This hint will trigger the creation of 24-byte records, instead of the standard 16. It should be the name of an attribute that will be used to generate the label for each record.

7.13.5.1.5. BIN_SORT

This hint will cause the records to be sorted. It should be the name of an attribute in the feature type.

7.13.5.1.6. BIN_BATCH_SIZE

This hint controls the batch size used when generating BIN records.

7.13.5.2. Example Query

Scala
import java.io.ByteArrayOutputStream
import org.geotools.data.Transaction
import org.locationtech.geomesa.index.conf.QueryHints

query.getHints.put(QueryHints.BIN_TRACK, "name")

val reader = dataStore.getFeatureReader(query, Transaction.AUTO_COMMIT)
val os = new ByteArrayOutputStream()

while (reader.hasNext) {
  os.write(reader.next().getAttribute(0).asInstanceOf[Array[Byte]])
}
reader.close()

// process bytes appropriately