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BigQuery Storage Read API with Limit and Offset

How to apply Limit and Offset based data select using Bigquery Storage Rad Api ?
Below is the sample I am trying to read data from a BigQuery Table. It is fetching entire table and I can provide filters based on column values. But I want to apply LIMIT and OFFSET and provide Custom SQL for data fetch/read. Is it possible in Storage API ?
import com.google.api.gax.rpc.ServerStream;
import com.google.cloud.bigquery.storage.v1.AvroRows;
import com.google.cloud.bigquery.storage.v1.BigQueryReadClient;
import com.google.cloud.bigquery.storage.v1.CreateReadSessionRequest;
import com.google.cloud.bigquery.storage.v1.DataFormat;
import com.google.cloud.bigquery.storage.v1.ReadRowsRequest;
import com.google.cloud.bigquery.storage.v1.ReadRowsResponse;
import com.google.cloud.bigquery.storage.v1.ReadSession;
import com.google.cloud.bigquery.storage.v1.ReadSession.TableModifiers;
import com.google.cloud.bigquery.storage.v1.ReadSession.TableReadOptions;
import com.google.common.base.Preconditions;
import com.google.protobuf.Timestamp;
import java.io.IOException;
import org.apache.avro.Schema;
import org.apache.avro.generic.GenericDatumReader;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.io.BinaryDecoder;
import org.apache.avro.io.DatumReader;
import org.apache.avro.io.DecoderFactory;
public class StorageSample {
/*
* SimpleRowReader handles deserialization of the Avro-encoded row blocks transmitted
* from the storage API using a generic datum decoder.
*/
private static class SimpleRowReader {
private final DatumReader<GenericRecord> datumReader;
// Decoder object will be reused to avoid re-allocation and too much garbage collection.
private BinaryDecoder decoder = null;
// GenericRecord object will be reused.
private GenericRecord row = null;
public SimpleRowReader(Schema schema) {
Preconditions.checkNotNull(schema);
datumReader = new GenericDatumReader<>(schema);
}
/**
* Sample method for processing AVRO rows which only validates decoding.
*
* #param avroRows object returned from the ReadRowsResponse.
*/
public void processRows(AvroRows avroRows) throws IOException {
decoder =
DecoderFactory.get()
.binaryDecoder(avroRows.getSerializedBinaryRows().toByteArray(), decoder);
while (!decoder.isEnd()) {
// Reusing object row
row = datumReader.read(row, decoder);
System.out.println(row.toString());
}
}
}
public static void main(String... args) throws Exception {
// Sets your Google Cloud Platform project ID.
// String projectId = "YOUR_PROJECT_ID";
String projectId = "gcs-test";
Integer snapshotMillis = null;
// if (args.length > 1) {
// snapshotMillis = Integer.parseInt(args[1]);
// }
try (BigQueryReadClient client = BigQueryReadClient.create()) {
String parent = String.format("projects/%s", projectId);
// This example uses baby name data from the public datasets.
String srcTable =
String.format(
"projects/%s/datasets/%s/tables/%s",
"gcs-test", "testdata", "testtable");
// We specify the columns to be projected by adding them to the selected fields,
// and set a simple filter to restrict which rows are transmitted.
TableReadOptions options =
TableReadOptions.newBuilder()
.addSelectedFields("id")
.addSelectedFields("qtr")
.addSelectedFields("sales")
.addSelectedFields("year")
.addSelectedFields("comments")
//.setRowRestriction("state = \"WA\"")
.build();
// Start specifying the read session we want created.
ReadSession.Builder sessionBuilder =
ReadSession.newBuilder()
.setTable(srcTable)
// This API can also deliver data serialized in Apache Avro format.
// This example leverages Apache Avro.
.setDataFormat(DataFormat.AVRO)
.setReadOptions(options);
// Optionally specify the snapshot time. When unspecified, snapshot time is "now".
if (snapshotMillis != null) {
Timestamp t =
Timestamp.newBuilder()
.setSeconds(snapshotMillis / 1000)
.setNanos((int) ((snapshotMillis % 1000) * 1000000))
.build();
TableModifiers modifiers = TableModifiers.newBuilder().setSnapshotTime(t).build();
sessionBuilder.setTableModifiers(modifiers);
}
// Begin building the session creation request.
CreateReadSessionRequest.Builder builder =
CreateReadSessionRequest.newBuilder()
.setParent(parent)
.setReadSession(sessionBuilder)
.setMaxStreamCount(1);
// Request the session creation.
ReadSession session = client.createReadSession(builder.build());
SimpleRowReader reader =
new SimpleRowReader(new Schema.Parser().parse(session.getAvroSchema().getSchema()));
// Assert that there are streams available in the session. An empty table may not have
// data available. If no sessions are available for an anonymous (cached) table, consider
// writing results of a query to a named table rather than consuming cached results directly.
Preconditions.checkState(session.getStreamsCount() > 0);
// Use the first stream to perform reading.
String streamName = session.getStreams(0).getName();
ReadRowsRequest readRowsRequest =
ReadRowsRequest.newBuilder().setReadStream(streamName).build();
// Process each block of rows as they arrive and decode using our simple row reader.
ServerStream<ReadRowsResponse> stream = client.readRowsCallable().call(readRowsRequest);
for (ReadRowsResponse response : stream) {
Preconditions.checkState(response.hasAvroRows());
reader.processRows(response.getAvroRows());
}
}
}
}

With the BigQuery Storage read API functionality, LIMIT is effectively just a case of stopping row reading after you've processed the desired number of elements.
Applying the notion of an OFFSET clause is a bit more nuanced, as it implies ordering. If you're reading a table via multiple streams for improved throughput, you're either disregarding ordering entirely, or you're re-ordering data after you've read it from the API.
If you read a table as a single string, you preserve whatever ordering the input table had, and can specify the offset field in the ReadRowsRequest to start at a given offset.

How to use QueryParser for Lucene range queries (IntPoint/LongPoint)

One thing I really like about Lucene is the query language where I/an application user) can write dynamic queries. I parse these queries via
QueryParser parser = new QueryParser("", indexWriter.getAnalyzer());
Query query = parser.parse("id:1 OR id:3");
But this does not work for range queries like these one:
Query query = parser.parse("value:[100 TO 202]"); // Returns nothing
Query query = parser.parse("id:1 OR value:167"); // Returns only document with ID 1 and not 1
On the other hand, via API it works (But I give up the convenient way to just use the query as input):
Query query = LongPoint.newRangeQuery("value", 100L, 202L); // Returns 1, 2 and 3
Is this a bug in query parser or do I miss an important point, like QueryParser takes the lexical and not numerical value? How can I chance this without using the query API but parsing the string?
The question is a follow up to this question that pointed out the problem, but not the reason: Lucene LongPoint Range search doesn't work
Full code:
package acme.prod;
import org.apache.lucene.analysis.standard.StandardAnalyzer;
import org.apache.lucene.document.*;
import org.apache.lucene.index.DirectoryReader;
import org.apache.lucene.index.IndexReader;
import org.apache.lucene.index.IndexWriter;
import org.apache.lucene.index.IndexWriterConfig;
import org.apache.lucene.queryparser.classic.QueryParser;
import org.apache.lucene.search.IndexSearcher;
import org.apache.lucene.search.Query;
import org.apache.lucene.search.TopDocs;
import org.apache.lucene.store.Directory;
import org.apache.lucene.store.RAMDirectory;
import java.util.Arrays;
import java.util.List;
import java.util.UUID;
public class LuceneRangeExample {
public static void main(String[] arguments) throws Exception {
// Create the index
Directory searchDirectoryIndex = new RAMDirectory();
IndexWriter indexWriter = new IndexWriter(searchDirectoryIndex, new IndexWriterConfig(new StandardAnalyzer()));
// Add several documents that have and ID and a value
List<Long> values = Arrays.asList(23L, 145L, 167L, 201L, 20100L);
int counter = 0;
for (Long value : values) {
Document document = new Document();
document.add(new StringField("id", Integer.toString(counter), Field.Store.YES));
document.add(new LongPoint("value", value));
document.add(new StoredField("value", Long.toString(value)));
indexWriter.addDocument(document);
indexWriter.commit();
counter++;
}
// Create the reader and search for the range 100 to 200
IndexReader indexReader = DirectoryReader.open(indexWriter);
IndexSearcher indexSearcher = new IndexSearcher(indexReader);
QueryParser parser = new QueryParser("", indexWriter.getAnalyzer());
// Query query = parser.parse("id:1 OR value:167");
// Query query = parser.parse("value:[100 TO 202]");
Query query = LongPoint.newRangeQuery("value", 100L, 202L);
TopDocs hits = indexSearcher.search(query, 100);
for (int i = 0; i < hits.scoreDocs.length; i++) {
int docid = hits.scoreDocs[i].doc;
Document document = indexSearcher.doc(docid);
System.out.println("ID: " + document.get("id") + " with range value " + document.get("value"));
}
}
}

I think there are a few different things to note here:
1. Using the classic parser
As you show in your question, the classic parser supports range searches, as documented here. But the key point to note in the documentation is:
Sorting is done lexicographically.
That is to say, it uses text-based sorting to determine whether a field's values are within the range or not.
However, your field is a LongPoint field (again, as you show in your code). This field stores your data as an array of longs, as shown in the constructor.
This is not lexicographical data - and even when you only have one value, it's not handled as string data.
I assume that this is why the following queries do not work as expected - but I am not 100% sure of this, because I did not find any documentation confirming this:
Query query = parser.parse("id:1 OR value:167");
Query query = parser.parse("value:[100 TO 202]");
(I am slightly surprised that these queries do not throw errors).
2. Using a LongPoint Query
As you have also shown, you can use one of the specialized LongPoint queries to get the results you expect - in your case, you used LongPoint.newRangeQuery("value", 100L, 202L);.
But as you also note, you lose the benefits of the classic parser syntax.
3. Using the Standard Query Parser
This may be a good approach which allows you to continue using your preferred syntax, while also supporting number-based range searches.
The StandardQueryParser is a newer alternative to the classic parser, but it uses the same syntax as the classic parser by default.
This parser lets you configure a "points config map", which tells the parser which fields to handle as numeric data, for operations such as range searches.
For example:
import org.apache.lucene.queryparser.flexible.standard.StandardQueryParser;
import org.apache.lucene.queryparser.flexible.standard.config.PointsConfig;
import java.text.DecimalFormat;
import java.util.Map;
import java.util.HashMap;
...
StandardQueryParser parser = new StandardQueryParser();
parser.setAnalyzer(indexWriter.getAnalyzer());
// Here I am just using the default decimal format - but you can provide
// a specific format string, as needed:
PointsConfig pointsConfig = new PointsConfig(new DecimalFormat(), Long.class);
Map<String, PointsConfig> pointsConfigMap = new HashMap<>();
pointsConfigMap.put("value", pointsConfig);
parser.setPointsConfigMap(pointsConfigMap);
Query query1 = parser.parse("value:[101 TO 203]", "");
Running your index searcher code with the above query gives the following output:
ID: 1 with range value 145
ID: 2 with range value 167
ID: 3 with range value 201
Note that this correctly excludes the 20100L value (which would be included if the query was using lexical sorting).
I don't know of any way to get the same results using only the classic query parser - but at least this is using the same query syntax that you would prefer to use.

compile and execute java program with external jar file

I understand this has been asked for multiple times, but I am really stuck here and if it is fairly easy, please help me.
I have a sample java program and a jar file.
Here is what is inside of the java program (WriterSample.java).
// (c) Copyright 2014. TIBCO Software Inc. All rights reserved.
package com.spotfire.samples;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStream;
import java.util.Date;
import java.util.Random;
import com.spotfire.sbdf.BinaryWriter;
import com.spotfire.sbdf.ColumnMetadata;
import com.spotfire.sbdf.FileHeader;
import com.spotfire.sbdf.TableMetadata;
import com.spotfire.sbdf.TableMetadataBuilder;
import com.spotfire.sbdf.TableWriter;
import com.spotfire.sbdf.ValueType;
/**
* This example is a simple command line tool that writes a simple SBDF file
* with random data.
*/
public class WriterSample {
public static void main(String[] args) throws IOException {
// The command line application requires one argument which is supposed to be
// the name of the SBDF file to write.
if (args.length != 1)
{
System.out.println("Syntax: WriterSample output.sbdf");
return;
}
String outputFile = args[0];
// First we just open the file as usual and then we need to wrap the stream
// in a binary writer.
OutputStream outputStream = new FileOutputStream(outputFile);
BinaryWriter writer = new BinaryWriter(outputStream);
// When writing an SBDF file you first need to write the file header.
FileHeader.writeCurrentVersion(writer);
// The second part of the SBDF file is the metadata, in order to create
// the table metadata we need to use the builder class.
TableMetadataBuilder tableMetadataBuilder = new TableMetadataBuilder();
// The table can have metadata properties defined. Here we add a custom
// property indicating the producer of the file. This will be imported as
// a table property in Spotfire.
tableMetadataBuilder.addProperty("GeneratedBy", "WriterSample.exe");
// All columns in the table needs to be defined and added to the metadata builder,
// the required information is the name of the column and the data type.
ColumnMetadata col1 = new ColumnMetadata("Category", ValueType.STRING);
tableMetadataBuilder.addColumn(col1);
// Similar to tables, columns can also have metadata properties defined. Here
// we add another custom property. This will be imported as a column property
// in Spotfire.
col1.addProperty("SampleProperty", "col1");
ColumnMetadata col2 = new ColumnMetadata("Value", ValueType.DOUBLE);
tableMetadataBuilder.addColumn(col2);
col2.addProperty("SampleProperty", "col2");
ColumnMetadata col3 = new ColumnMetadata("TimeStamp", ValueType.DATETIME);
tableMetadataBuilder.addColumn(col3);
col3.addProperty("SampleProperty", "col3");
// We need to call the build function in order to get an object that we can
// write to the file.
TableMetadata tableMetadata = tableMetadataBuilder.build();
tableMetadata.write(writer);
int rowCount = 10000;
Random random = new Random();
// Now that we have written all the metadata we can start writing the actual data.
// Here we use a TableWriter to write the data, remember to close the table writer
// otherwise you will not generate a correct SBDF file.
TableWriter tableWriter = new TableWriter(writer, tableMetadata);
for (int i = 0; i < rowCount; ++i) {
// You need to perform one addValue call for each column, for each row in the
// same order as you added the columns to the table metadata object.
// In this example we just generate some random values of the appropriate types.
// Here we write the first string column.
String[] col1Values = new String[] {"A", "B", "C", "D", "E"};
tableWriter.addValue(col1Values[random.nextInt(5)]);
// Next we write the second double column.
double doubleValue = random.nextDouble();
if (doubleValue < 0.5) {
// Note that if you want to write a null value you shouldn't send null to
// addValue, instead you should use theInvalidValue property of the columns
// ValueType.
tableWriter.addValue(ValueType.DOUBLE.getInvalidValue());
} else {
tableWriter.addValue(random.nextDouble());
}
// And finally the third date time column.
tableWriter.addValue(new Date());
}
// Finally we need to close the file and write the end of table marker.
tableWriter.writeEndOfTable();
writer.close();
outputStream.close();
System.out.print("Wrote file: ");
System.out.println(outputFile);
}
}
The jar file is sbdf.jar, which is in the same directory as the java file.
I can now compile with:
javac -cp "sbdf.jar" WriterSample.java
This will generate a WriterSample.class file.
The problem is that when I try to execute the program by
java -cp .:./sbdf.jar WriterSample
I got an error message:
Error: Could not find or load main class WriterSample
What should I do? Thanks!

You should use the fully qualified name of the WriterSample, which is com.spotfire.samples.WriterSample and the correct java command is:
java -cp .:././sbdf.jar com.spotfire.samples.WriterSample

How to consume a H2OModel computed and saved in Python API

I've been reading the H2O documentation for a while, and I haven't found a clear example of how to load model trained and saved using the Python API. I was following the next example.
import h2o
from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator
model = H2ONaiveBayesEstimator()
h2o_df = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")
model.train(y = "IsDepDelayed", x = ["Year", "Origin"],
training_frame = h2o_df,
family = "binomial",
lambda_search = True,
max_active_predictors = 10)
h2o.save_model(model, path=models)
But if you check the official documentation it states that you have to download the model as a POJO from the flow UI. Is it the only way? or, may I achieve the same result via python? Just for information, I show the doc's example below. I need some guidance.
import java.io.*;
import hex.genmodel.easy.RowData;
import hex.genmodel.easy.EasyPredictModelWrapper;
import hex.genmodel.easy.prediction.*;
public class main {
private static String modelClassName = "gbm_pojo_test";
public static void main(String[] args) throws Exception {
hex.genmodel.GenModel rawModel;
rawModel = (hex.genmodel.GenModel) Class.forName(modelClassName).newInstance();
EasyPredictModelWrapper model = new EasyPredictModelWrapper(rawModel);
//
// By default, unknown categorical levels throw PredictUnknownCategoricalLevelException.
// Optionally configure the wrapper to treat unknown categorical levels as N/A instead:
//
// EasyPredictModelWrapper model = new EasyPredictModelWrapper(
// new EasyPredictModelWrapper.Config()
// .setModel(rawModel)
// .setConvertUnknownCategoricalLevelsToNa(true));
RowData row = new RowData();
row.put("Year", "1987");
row.put("Month", "10");
row.put("DayofMonth", "14");
row.put("DayOfWeek", "3");
row.put("CRSDepTime", "730");
row.put("UniqueCarrier", "PS");
row.put("Origin", "SAN");
row.put("Dest", "SFO");
BinomialModelPrediction p = model.predictBinomial(row);
System.out.println("Label (aka prediction) is flight departure delayed: " + p.label);
System.out.print("Class probabilities: ");
for (int i = 0; i < p.classProbabilities.length; i++) {
if (i > 0) {
System.out.print(",");
}
System.out.print(p.classProbabilities[i]);
}
System.out.println("");
}
}

h2o.save_model will save the binary model to the provided file system, however, looking at the Java application above it seems you want to use model into a Java based scoring application.
Because of that you should be using h2o.download_pojo API to save the model to local file system along with genmodel jar file. The API is documented as below:
download_pojo(model, path=u'', get_jar=True)
Download the POJO for this model to the directory specified by the path; if the path is "", then dump to screen.
:param model: the model whose scoring POJO should be retrieved.
:param path: an absolute path to the directory where POJO should be saved.
:param get_jar: retrieve the h2o-genmodel.jar also.
Once you have download POJO, you can use the above sample application to perform the scoring and make sure the POJO class name and the "modelClassName" are same along with model type.

SQL parser library for Java - Retrieve the list of table names present in a SQL statement

I am looking for a SQL Library that will parse an SQL statement and return some sort of Object representation of the SQL statement. My main objective is actually to be able to parse the SQL statement and retrieve the list of table names present in the SQL statement (including subqueries, joins and unions).
I am looking for a free library with a license business friendly (e.g. Apache license). I am looking for a library and not for an SQL Grammar. I do not want to build my own parser.
The best I could find so far was JSQLParser, and the example they give is actually pretty close to what I am looking for. However it fails parsing too many good queries (DB2 Database) and I'm hoping to find a more reliable library.

I doubt you'll find anything prewritten that you can just use. The problem is that ISO/ANSI SQL is a very complicated grammar — something like more than 600 production rules IIRC.
Terence Parr's ANTLR parser generator (Java, but can generate parsers in any one of a number of target languages) has several SQL grammars available, including a couple for PL/SQL, one for a SQL Server SELECT statement, one for mySQL, and one for ISO SQL.
No idea how complete/correct/up-to-date they are.
http://www.antlr.org/grammar/list

You needn't reinvent the wheel, there is already such a reliable SQL parser library there, (it's commerical, not free), and this article shows how to retrieve the list of table names present in the SQL statement (including subqueries, joins and unions) that is exactly what you are looking for.
http://www.dpriver.com/blog/list-of-demos-illustrate-how-to-use-general-sql-parser/get-columns-and-tables-in-sql-script/
This SQL parser library supports Oracle, SQL Server, DB2, MySQL, Teradata and ACCESS.

You need the ultra light, ultra fast library to extract table names from SQL (Disclaimer: I am the owner)
Just add the following in your pom
<dependency>
<groupId>com.github.mnadeem</groupId>
<artifactId>sql-table-name-parser</artifactId>
<version>0.0.1</version>
And do the following
new TableNameParser(sql).tables()
For more details, refer the project

Old question, but I think this project contains what you need:
Data Tools Project - SQL Development Tools
Here's the documentation for the SQL Query Parser.
Also, here's a small sample program. I'm no Java programmer so use with care.
package org.lala;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.nio.MappedByteBuffer;
import java.nio.channels.FileChannel;
import java.nio.charset.Charset;
import java.util.Iterator;
import java.util.List;
import org.eclipse.datatools.modelbase.sql.query.QuerySelectStatement;
import org.eclipse.datatools.modelbase.sql.query.QueryStatement;
import org.eclipse.datatools.modelbase.sql.query.TableReference;
import org.eclipse.datatools.modelbase.sql.query.ValueExpressionColumn;
import org.eclipse.datatools.modelbase.sql.query.helper.StatementHelper;
import org.eclipse.datatools.sqltools.parsers.sql.SQLParseErrorInfo;
import org.eclipse.datatools.sqltools.parsers.sql.SQLParserException;
import org.eclipse.datatools.sqltools.parsers.sql.SQLParserInternalException;
import org.eclipse.datatools.sqltools.parsers.sql.query.SQLQueryParseResult;
import org.eclipse.datatools.sqltools.parsers.sql.query.SQLQueryParserManager;
import org.eclipse.datatools.sqltools.parsers.sql.query.SQLQueryParserManagerProvider;
public class SQLTest {
private static String readFile(String path) throws IOException {
FileInputStream stream = new FileInputStream(new File(path));
try {
FileChannel fc = stream.getChannel();
MappedByteBuffer bb = fc.map(FileChannel.MapMode.READ_ONLY, 0,
fc.size());
/* Instead of using default, pass in a decoder. */
return Charset.defaultCharset().decode(bb).toString();
} finally {
stream.close();
}
}
/**
* #param args
* #throws IOException
*/
public static void main(String[] args) throws IOException {
try {
// Create an instance the Parser Manager
// SQLQueryParserManagerProvider.getInstance().getParserManager
// returns the best compliant SQLQueryParserManager
// supporting the SQL dialect of the database described by the given
// database product information. In the code below null is passed
// for both the database and version
// in which case a generic parser is returned
SQLQueryParserManager parserManager = SQLQueryParserManagerProvider
.getInstance().getParserManager("DB2 UDB", "v9.1");
// Sample query
String sql = readFile("c:\\test.sql");
// Parse
SQLQueryParseResult parseResult = parserManager.parseQuery(sql);
// Get the Query Model object from the result
QueryStatement resultObject = parseResult.getQueryStatement();
// Get the SQL text
String parsedSQL = resultObject.getSQL();
System.out.println(parsedSQL);
// Here we have the SQL code parsed!
QuerySelectStatement querySelect = (QuerySelectStatement) parseResult
.getSQLStatement();
List columnExprList = StatementHelper
.getEffectiveResultColumns(querySelect);
Iterator columnIt = columnExprList.iterator();
while (columnIt.hasNext()) {
ValueExpressionColumn colExpr = (ValueExpressionColumn) columnIt
.next();
// DataType dataType = colExpr.getDataType();
System.out.println("effective result column: "
+ colExpr.getName());// + " with data type: " +
// dataType.getName());
}
List tableList = StatementHelper.getTablesForStatement(resultObject);
// List tableList = StatementHelper.getTablesForStatement(querySelect);
for (Object obj : tableList) {
TableReference t = (TableReference) obj;
System.out.println(t.getName());
}
} catch (SQLParserException spe) {
// handle the syntax error
System.out.println(spe.getMessage());
#SuppressWarnings("unchecked")
List<SQLParseErrorInfo> syntacticErrors = spe.getErrorInfoList();
Iterator<SQLParseErrorInfo> itr = syntacticErrors.iterator();
while (itr.hasNext()) {
SQLParseErrorInfo errorInfo = (SQLParseErrorInfo) itr.next();
// Example usage of the SQLParseErrorInfo object
// the error message
String errorMessage = errorInfo.getParserErrorMessage();
String expectedText = errorInfo.getExpectedText();
String errorSourceText = errorInfo.getErrorSourceText();
// the line numbers of error
int errorLine = errorInfo.getLineNumberStart();
int errorColumn = errorInfo.getColumnNumberStart();
System.err.println("Error in line " + errorLine + ", column "
+ errorColumn + ": " + expectedText + " "
+ errorMessage + " " + errorSourceText);
}
} catch (SQLParserInternalException spie) {
// handle the exception
System.out.println(spie.getMessage());
}
System.exit(0);
}
}