In this part we will explore several Optimization techniques for CSVDataStore.

Query Hints

The GeoTools Hints system can be used to configure a DataStore for use by an application. This is often done to speed things up by providing the Factories that the DataStore will use during the course of its operation.

As an example a CurvedGeoemtryFactory with a specific tolerance can be passed in to aid in parsing WKT containing arcs:

Query query = new Query( typeName );
Hints hints = new Hints();
hints.put( Hints.JTS_GEOMETRY_FACTORY, new CurvedGeometryFactory( 0.005 ) );
SimpleFeatureCollection features = featureSource.getFeatures( query );

To interactively discover what hints are supported clients call FeatureSource.getSupportedHints().

At the time of writing the following hints are supported:

  • Hints.FEATURE_DETACHED: indicates returned features are a copy (can can be modified without side-effect)
  • Hints.JTS_GEOMETRY_FACTORY: control of geometry representation
  • Hints.JTS_COORDINATE_SEQUENCE_FACTORY: control of coordinate storage (you may be able to optimise read performance by directly using the binary data provided by your dataformat, or you may wish to optimise for memory use).
  • Hints.JTS_PRECISION_MODEL: configure to match precision maintained by coordinate sequence factory
  • Hints.JTS_SRID: for compatibility with systems using a spatial reference system identifier (such as PostGIS)
  • Hints.GEOMETRY_DISTANCE: used to select a geometry of appropriate generalization. Your datastore may wish to simplify “on the fly” while reading the geometry
  • Hints.FEATURE_2D: used to indicate that only two dimensions are required (ignoring the 3rd dimension for 2.5D data)

Many of these values are filled in when rendering, by taking advantage of these query hints you can offer vastly improved performance.


Your implementation can also advertise additional functionality using the FeatureSource.getQueryCapabilities() data structure.

Formats that allows user supplied FeatureIds when adding new features fill in QueryCapabilities.isUseProvidedFIDSupported() to return true.

To use this approach CSVDataStore would need to be extended with an FID_COLUMN parameter (to be used as a FeatureId). This works when reading (or modifying) existing features, but we run into a glitch in the API when inserting new features … the feature id cannot be changed!

The workaround is to ask clients to store the proposed FeatureId in the user data map:

public String[] encode(SimpleFeature feature) {
     List<String> csvRecord = new ArrayList<String>();

     String fid = feature.getId();
     if( Boolean.TRUE.equals( feature.getUserData().get(Hints.USE_PROVIDED_FID) ) ){
         if( feature.getUserData().containsKey(Hints.PROVIDED_FID)){
             fid = (String) feature.getUserData().get(Hints.PROVIDED_FID);

     for (Property property : feature.getProperties()) {
         Object value = property.getValue();
         if (value == null) {
         } else {
             String txt = value.toString();
     return csvRecord.toArray(new String[csvRecord.size()-1]);

Low-Level Optimization

Single Use Feature Writers

The first level of optimizations available is paying attention to the flags provided when setting up our CSVFeatureWriter.

The flags passed in provide a bit of context for how the FeatureWriter will be used - so if you have a better implementation on hand you are welcome to use it.

protected FeatureWriter<SimpleFeatureType, SimpleFeature> getWriterInternal(Query query,
         int flags) throws IOException {
    boolean append = (flags | WRITER_ADD) == WRITER_ADD;
    return new CSVFeatureWriter(getState(), query, append);

There are three distinct uses for FeatureWriters:

  • getFeatureWriter( typeName, transaction )

    General purpose FeatureWriter

  • getFeatureWriter( typeName, filter, transaction )

    An optimized version that does not create new content can be created.

  • getFeatureWriterAppend( typeName, transaction)

    An optimized version that duplicates the original file, and opens it in append mode can be created. We can also perform special tricks such as returning a Feature delegate to the user, which records when it has been modified.



Can you update the CSVFeatureWriter, or create a new one, that can quickly start appending content to the end of the file?


  • It is tempting to start with the use of Files.copy, but remember you need to track the number of features in order to generate FeatureIds when appending.
  • You may wish to review the implementation of FeatureWriters in ShapeDataStore and JDBCDataStore.

Wrapper/Decorator Optimization

ContentDataStore provides a lot of functionality based on the methods we implemented in the Tutorials. We also know there are a number of wrappers used to fill in the gaps in our functionality.

It is worth reviewing ContentFeatureSource.getReader(Query query) to see what wrappers may be in play.


Each wrapper represents a post-processing step that is being applied on your data. If you are making use of a service that supports reprojection - then you can implement canReproject() and avoid this overhead.

//apply wrappers based on subclass capabilities
// transactions
if( !canTransact() && transaction != null && transaction != Transaction.AUTO_COMMIT) {
    DiffTransactionState state = (DiffTransactionState) getTransaction().getState(getEntry());
    reader = new DiffFeatureReader<SimpleFeatureType, SimpleFeature>(reader, state.getDiff());

if ( !canFilter() ) {
    if (query.getFilter() != null && query.getFilter() != Filter.INCLUDE ) {
        reader = new FilteringFeatureReader<SimpleFeatureType, SimpleFeature>( reader, query.getFilter() );

if ( !canRetype() ) {
    if ( query.getPropertyNames() != Query.ALL_NAMES ) {
        //rebuild the type and wrap the reader
        SimpleFeatureType target =
            SimpleFeatureTypeBuilder.retype(getSchema(), query.getPropertyNames());

        // do an equals check because we may have needlessly retyped (that is,
        // the subclass might be able to only partially retype)
        if ( !target.equals( reader.getFeatureType() ) ) {
            reader = new ReTypeFeatureReader( reader, target, false );

// sorting
if ( query.getSortBy() != null && query.getSortBy().length != 0 ) {
    if ( !canSort() ) {
        reader = new SortedFeatureReader(DataUtilities.simple(reader), query);

// offset
int offset = query.getStartIndex() != null ? query.getStartIndex() : 0;
if( !canOffset() && offset > 0 ) {
    // skip the first n records
    for(int i = 0; i < offset && reader.hasNext(); i++) {

// max feature limit
if ( !canLimit() ) {
    if (query.getMaxFeatures() != -1 && query.getMaxFeatures() < Integer.MAX_VALUE ) {
        reader = new MaxFeatureReader<SimpleFeatureType, SimpleFeature>(reader, query.getMaxFeatures());

// reprojection
if ( !canReproject() ) {
    CoordinateReferenceSystem targetCRS = query.getCoordinateSystemReproject();
    if (targetCRS != null) {
        CoordinateReferenceSystem nativeCRS = reader.getFeatureType().getCoordinateReferenceSystem();
        if(nativeCRS == null) {
            throw new IOException("Cannot reproject data, the source CRS is not available");
        } else if(!nativeCRS.equals(targetCRS)) {
            try {
                reader = new ReprojectFeatureReader(reader, targetCRS);
            } catch (Exception e) {
                if(e instanceof IOException)
                    throw (IOException) e;
                    throw (IOException) new IOException("Error occurred trying to reproject data").initCause(e);



The canRetype() operations is easy to support, check the query and only provide values for the requested attributes. This is an especially valuable Optimization to perform at a low-level as you may be able to avoid an expensive step (like parsing Geometry) if it is not being requested by the client.


  • Check the Query object passed into your FeatureWriter

A similar set of wrappers is used for FeatureWriter:

public final FeatureWriter<SimpleFeatureType, SimpleFeature> getWriter( Query query, int flags ) throws IOException {
    query = joinQuery( query );
    query = resolvePropertyNames(query);

    FeatureWriter<SimpleFeatureType, SimpleFeature> writer;

    if (!canTransact() && transaction != null && transaction != Transaction.AUTO_COMMIT) {
        DiffTransactionState state = (DiffTransactionState) getTransaction().getState(getEntry());
        FeatureReader<SimpleFeatureType, SimpleFeature> reader = getReader(query);
        writer = new DiffContentFeatureWriter(this, state.getDiff(), reader);
    } else {
        writer = getWriterInternal(query, flags);

        // events
        if (!canEvent()){
            writer = new EventContentFeatureWriter(this, writer );
        // filtering
        if (!canFilter()) {
            if (query.getFilter() != null && query.getFilter() != Filter.INCLUDE) {
                writer = new FilteringFeatureWriter(writer, query.getFilter());

        // Use InProcessLockingManager to assert write locks?
        if (!canLock()) {
            LockingManager lockingManager = getDataStore().getLockingManager();
            writer = ((InProcessLockingManager) lockingManager).checkedWriter(writer,

    // Finished
    return writer;

The wrapper classes mentioned above are excellent examples on how to create your own FeatureWriters.


Historically Filter.ALL and Filter.NONE were used as placeholder, as crazy as it sounds, Filter.ALL filters out ALL (accepts none) Filter.NONE filters out NONE (accepts ALL).

These two have been renamed in GeoTools 2.3 for the following:

  • Filter.ALL has been replaced with Filter.EXCLUDE
  • Filter.NONE has been replaced with Filter.INCLUDE

Every helper class we discussed above can be replaced if your external data source supports the functionality.

Custom ContentState

JDBDataStore supplies an example of subclassing ContentEntry to store additional information.





Create your own CSVState and wire it into CSVDataStore.

If you like you can use your CSVState to store a SpatialIndex listing the row numbers.

High-Level Optimization

DataStore, FeatureSource and FeatureStore provide a few methods specifically set up for Optimization.

DataStore Optimization

DataStore leaves open a number of methods for high-level optimisations:

  • ContentDataStore.getCount( query )
  • ContentDataStore.getBounds( query )

ContentDataStore has already done a good job of isolating this calculation and recording the result on ContentState (so it is not regenerated each time).

FeatureStore Optimization

DataStores operating against rich external data sources can often perform high level Optimizations. JDBCDataStores for instance can often construct SQL statements that completely fulfill a request without making use of FeatureWriters at all.

When performing these queries please remember two things:

  1. Check the lockingManager - If you are not providing your own native locking support, please check the user’s authorisation against the the lockingManager
  2. Event Notification - Remember to fire the appropriate notification events when contents change, Feature Caches will depend on this notification to accurately track the contents of your DataStore



Since the FeatureId for CSV files is determined by row number, you can quickly scan to to a requested FeatureID by skipping an appropriate number of rows.

Use this knowledge to implement an optimized version of FeatureSource.removeFeatures(Filter filter) that detects the use of an Id filter.

Hint: The Id Filter contains a Set<FeatureId> - and you deliberately constructed your FeatureId with a consistent pattern.