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https://issues.apache.org/jira/browse/DRILL-8474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17807233#comment-17807233
]
ASF GitHub Bot commented on DRILL-8474:
---------------------------------------
mbeckerle commented on code in PR #2836:
URL: https://github.com/apache/drill/pull/2836#discussion_r1453422371
##########
contrib/format-daffodil/src/main/java/org/apache/drill/exec/store/daffodil/DaffodilBatchReader.java:
##########
@@ -0,0 +1,181 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.drill.exec.store.daffodil;
+
+import org.apache.daffodil.japi.DataProcessor;
+import org.apache.drill.common.AutoCloseables;
+import org.apache.drill.common.exceptions.CustomErrorContext;
+import org.apache.drill.common.exceptions.UserException;
+import org.apache.drill.exec.physical.impl.scan.v3.ManagedReader;
+import org.apache.drill.exec.physical.impl.scan.v3.file.FileDescrip;
+import org.apache.drill.exec.physical.impl.scan.v3.file.FileSchemaNegotiator;
+import org.apache.drill.exec.physical.resultSet.RowSetLoader;
+import org.apache.drill.exec.record.metadata.TupleMetadata;
+import
org.apache.drill.exec.store.daffodil.schema.DaffodilDataProcessorFactory;
+import org.apache.drill.exec.store.dfs.DrillFileSystem;
+import org.apache.drill.exec.store.dfs.easy.EasySubScan;
+import org.apache.hadoop.fs.Path;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.io.IOException;
+import java.io.InputStream;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.Objects;
+
+import static
org.apache.drill.exec.store.daffodil.schema.DaffodilDataProcessorFactory.*;
+import static
org.apache.drill.exec.store.daffodil.schema.DrillDaffodilSchemaUtils.daffodilDataProcessorToDrillSchema;
+
+public class DaffodilBatchReader implements ManagedReader {
+
+ private static final Logger logger =
LoggerFactory.getLogger(DaffodilBatchReader.class);
+ private final RowSetLoader rowSetLoader;
+ private final CustomErrorContext errorContext;
+ private final DaffodilMessageParser dafParser;
+ private final InputStream dataInputStream;
+
+ public DaffodilBatchReader(DaffodilReaderConfig readerConfig, EasySubScan
scan,
+ FileSchemaNegotiator negotiator) {
+
+ errorContext = negotiator.parentErrorContext();
+ DaffodilFormatConfig dafConfig = readerConfig.plugin.getConfig();
+
+ String schemaURIString = dafConfig.getSchemaURI(); //
"schema/complexArray1.dfdl.xsd";
+ String rootName = dafConfig.getRootName();
+ String rootNamespace = dafConfig.getRootNamespace();
+ boolean validationMode = dafConfig.getValidationMode();
+
+ URI dfdlSchemaURI;
+ try {
+ dfdlSchemaURI = new URI(schemaURIString);
+ } catch (URISyntaxException e) {
+ throw UserException.validationError(e).build(logger);
+ }
+
+ FileDescrip file = negotiator.file();
+ DrillFileSystem fs = file.fileSystem();
+ URI fsSchemaURI = fs.getUri().resolve(dfdlSchemaURI);
+
+ DaffodilDataProcessorFactory dpf = new DaffodilDataProcessorFactory();
+ DataProcessor dp;
+ try {
+ dp = dpf.getDataProcessor(fsSchemaURI, validationMode, rootName,
rootNamespace);
+ } catch (CompileFailure e) {
+ throw UserException.dataReadError(e)
+ .message(String.format("Failed to get Daffodil DFDL processor for:
%s", fsSchemaURI))
+ .addContext(errorContext).addContext(e.getMessage()).build(logger);
+ }
+ // Create the corresponding Drill schema.
+ // Note: this could be a very large schema. Think of a large complex RDBMS
schema,
+ // all of it, hundreds of tables, but all part of the same metadata tree.
+ TupleMetadata drillSchema = daffodilDataProcessorToDrillSchema(dp);
+ // Inform Drill about the schema
+ negotiator.tableSchema(drillSchema, true);
+
+ //
+ // DATA TIME: Next we construct the runtime objects, and open files.
+ //
+ // We get the DaffodilMessageParser, which is a stateful driver for
daffodil that
+ // actually does the parsing.
+ rowSetLoader = negotiator.build().writer();
+
+ // We construct the Daffodil InfosetOutputter which the daffodil parser
uses to
+ // convert infoset event calls to fill in a Drill row via a rowSetLoader.
+ DaffodilDrillInfosetOutputter outputter = new
DaffodilDrillInfosetOutputter(rowSetLoader);
+
+ // Now we can set up the dafParser with the outputter it will drive with
+ // the parser-produced infoset.
+ dafParser = new DaffodilMessageParser(dp); // needs further initialization
after this.
+ dafParser.setInfosetOutputter(outputter);
+
+ Path dataPath = file.split().getPath();
+ // Lastly, we open the data stream
+ try {
+ dataInputStream = fs.openPossiblyCompressedStream(dataPath);
+ } catch (IOException e) {
+ throw UserException.dataReadError(e)
+ .message(String.format("Failed to open input file: %s",
dataPath.toString()))
+ .addContext(errorContext).addContext(e.getMessage()).build(logger);
+ }
+ // And lastly,... tell daffodil the input data stream.
+ dafParser.setInputStream(dataInputStream);
+ }
+
+ /**
+ * This is the core of actual processing - data movement from Daffodil to
Drill.
+ * <p>
+ * If there is space in the batch, and there is data available to parse then
this calls the
+ * daffodil parser, which parses data, delivering it to the rowWriter by way
of the infoset
+ * outputter.
+ * <p>
+ * Repeats until the rowWriter is full (a batch is full), or there is no
more data, or a parse
+ * error ends execution with a throw.
+ * <p>
+ * Validation errors and other warnings are not errors and are logged but do
not cause parsing to
+ * fail/throw.
+ *
+ * @return true if there are rows retrieved, false if no rows were
retrieved, which means no more
+ * will ever be retrieved (end of data).
+ * @throws RuntimeException
+ * on parse errors.
+ */
+ @Override
+ public boolean next() {
+ // Check assumed invariants
+ // We don't know if there is data or not. This could be called on an empty
data file.
+ // We DO know that this won't be called if there is no space in the batch
for even 1
+ // row.
+ if (dafParser.isEOF()) {
+ return false; // return without even checking for more rows or trying to
parse.
+ }
+ while (rowSetLoader.start() && !dafParser.isEOF()) { // we never zero-trip
this loop.
+ // the predicate is always true once.
+ dafParser.parse();
+ if (dafParser.isProcessingError()) {
+ assert (Objects.nonNull(dafParser.getDiagnostics()));
+ throw
UserException.dataReadError().message(dafParser.getDiagnosticsAsString())
+ .addContext(errorContext).build(logger);
+ }
+ if (dafParser.isValidationError()) {
+ logger.warn(dafParser.getDiagnosticsAsString());
Review Comment:
Agree.
We draw a distinction between "well formed" and "invalid" data and whether
one does validation seems like the right switch in daffodil to use.
If data is malformed, that means you can't successfully parse it. If it is
invalid, that just means values are unexpected. Example: A 3 digit number
representing a percentage 0 to 100. -1 is invalid, ABC is malformed.
If data is not well formed, you really cannot continue parsing it, as you
cannot convert it to the type expected. But, if you are able to determine at
least how big it is, it's possible to capture that length of data into a dummy
"badData" element which is always invalid (so isn't a "false positive" parse).
This capability has to be designed into the DFDL schema, but it is something
we've been doing more and more.
Hence, one can tolerate even _some_ malformed data. If it is malformed to
where you cannot determine the length, then continuing is impossible.
We will see if more than this is needed. Options like the "use all
strings/varchar" or all numbers are float, which you have for toleratng
situations with other data connectors *may* prove useful, particularly while a
DFDL schema is in development and you are really just testing it (and the
corresponding data) using Drill.
> Add Daffodil Format Plugin
> --------------------------
>
> Key: DRILL-8474
> URL: https://issues.apache.org/jira/browse/DRILL-8474
> Project: Apache Drill
> Issue Type: New Feature
> Affects Versions: 1.21.1
> Reporter: Charles Givre
> Priority: Major
> Fix For: 1.22.0
>
>
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