File to DB load using Apache beam - google-cloud-platform

I need to load a file into my database, but before that I have to verify data is present in the database based on some file data. For instance, suppose I have 5 records in a file then I have to check 5 times in the database for separate records.
So how can I get this value dynamically? We have to pass dynamic value instead of 2 in line (preparedStatement.setString(1, "2");)
Here we are creating a Dataflow pipeline which loads data into the database using Apache Beam. Now we create a pipeline object and create a pipeline. Using a PCollection we are storing into database.
Pipeline p = Pipeline.create(options);
p.apply("Reading Text", TextIO.read().from(options.getInputFile()))
.apply(ParDo.of(new FilterHeaderFn(csvHeader)))
.apply(ParDo.of(new GetRatePlanID()))
.apply("Format Result", MapElements.into(
TypeDescriptors.strings()).via(
(KV < String, Integer > ABC) - >
ABC.getKey() + "," + ABC.getValue()))
.apply("Write File", TextIO.write()
.to(options.getOutputFile())
.withoutSharding());
// Retrieving data from database
PCollection < String > data =
p.apply(JdbcIO. < String > read()
.withDataSourceConfiguration(JdbcIO.DataSourceConfiguration.create(
"com.mysql.cj.jdbc.Driver", "jdbc:mysql://localhost:3306/XYZ")
.withUsername("root")
.withPassword("root1234"))
.withQuery("select * from xyz where z = ?")
.withCoder(StringUtf8Coder.of())
.withStatementPreparator(new JdbcIO.StatementPreparator() {
private static final long serialVersionUID = 1 L;
#Override
public void setParameters(PreparedStatement preparedStatement) throws Exception {
preparedStatement.setString(1, "2");
}
})
.withRowMapper(new JdbcIO.RowMapper < String > () {
private static final long serialVersionUID = 1 L;
public String mapRow(ResultSet resultSet) throws Exception {
return "Symbol: " + resultSet.getInt(1) + "\nPrice: " + resultSet.getString(2) +
"\nCompany: " + resultSet.getInt(3);
}
}));

As suggested, the most efficient would probably be loading the whole file into a temporary table and then doing a query to update the requisite rows.
If that can't be done, you could instead read the table into Dataflow (i.e. "select * from xyz") and then do a join/CoGroupByKey to match records with those found in your file. If you expect the existing database to be very large compared to the files you're hoping to upload into it, you could have a DoFn that makes queries to your database directly using JDBC (possibly caching the connection in the DoFn's setUp method) rather than using JdbcIO.

Related

Save Array<T> in BigQuery using Java

I'm trying to save data into Big query using Spark Big Query connector. Let say I have a Java pojo like below
#Getter
#Setter
#AllArgsConstructor
#ToString
#Builder
public class TagList {
private String s1;
private List<String> s2;
}
Now when I try to save this Pojo into Big query its throwing me below error
Caused by: com.google.cloud.spark.bigquery.repackaged.com.google.cloud.bigquery.BigQueryException: Failed to load to test_table1 in job JobId{project=<project_id>, job=<job_id>, location=US}. BigQuery error was Provided Schema does not match Table <Table_Name>. Field s2 has changed type from STRING to RECORD
at com.google.cloud.spark.bigquery.BigQueryWriteHelper.loadDataToBigQuery(BigQueryWriteHelper.scala:156)
at com.google.cloud.spark.bigquery.BigQueryWriteHelper.writeDataFrameToBigQuery(BigQueryWriteHelper.scala:89)
... 35 more
Sample code:
Dataset<TagList> mapDS = inputDS.map((MapFunction<Row, TagList>) x -> {
List<String> list = new ArrayList<>();
list.add(x.get(0).toString());
list.add("temp1");
return TagList.builder()
.s1("Hello World")
.s2(list).build();
}, Encoders.bean(TagList.class));
mapDS.write().format("bigquery")
.option("temporaryGcsBucket","<bucket_name>")
.option("table", "<table_name>")
.option("project", projectId)
.option("parentProject", projectId)
.mode(SaveMode.Append)
.save();
Big Query Table:
create table <dataset>.<table_name> (
s1 string,
s2 array<string>,
)
PARTITION BY
TIMESTAMP_TRUNC(_PARTITIONTIME, HOUR);
Please change the intermediateFormat to AVRO or ORC. When using Parquet, the serialization creates an intermediate structure. See more at https://github.com/GoogleCloudDataproc/spark-bigquery-connector#properties

Passing Side Input in PCollection Partition

I want to pass a sideInput in PCollection Partition and On basis of that, i need to Divide my PCollection is their anyway....
PCollectionList<TableRow> part = merged.apply(Partition.of(Pcollection Count Function Called, new PartitionFn<TableRow>(){
#Override
public int partitionFor(TableRow arg0, int arg1) {
return 0;
}
}));
Any Other Way through Which I Can Partition My PCollection
//Without Dynamic destination partitioning BigQuery table
merge.apply("write into target", BigQueryIO.writeTableRows()
.to(new SerializableFunction<ValueInSingleWindow<TableRow>, TableDestination>() {
#Override
public TableDestination apply(ValueInSingleWindow<TableRow> value) {
TableRow row = value.getValue();
TableReference reference = new TableReference();
reference.setProjectId("XYZ");
reference.setDatasetId("ABC");
System.out.println("date of row " + row.get("authorized_transaction_date_yyyymmdd").toString());
LOG.info("date of row "+
row.get("authorized_transaction_date_yyyymmdd").toString());
String str = row.get("authorized_transaction_date_yyyymmdd").toString();
str = str.substring(0, str.length() - 2) + "01";
System.out.println("str value " + str);
LOG.info("str value " + str);
reference.setTableId("TargetTable$" + str);
return new TableDestination(reference, null);
}
}).withFormatFunction(new SerializableFunction<TableRow, TableRow>() {
#Override
public TableRow apply(TableRow input) {
LOG.info("format function:"+input.toString());
return input;
}
})
.withSchema(schema1).withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED));
Now I have to use Dynamic Destination Any Solution.Insted Of this and have to Do Partition.
Based on seeing TableRow in your code, I suspect that you want to write a PCollection to BigQuery, sending different elements to different BigQuery tables. BigQueryIO.write() already provides a method to do that, using BigQueryIO.write().to(DynamicDestinations). See Writing different values to different BigQuery tables in Apache Beam.

How to do multiple parallel readers for data export using Google Spanner?

External Backups/Snapshots for Google Cloud Spanner recommends to use queries with timestamp bounds to create snapshots for export. On the bottom of the Timestamp Bounds documentation it states:
Cloud Spanner continuously garbage collects deleted and overwritten data in the background to reclaim storage space. This process is known as version GC. By default, version GC reclaims versions after they are one hour old. Because of this, Cloud Spanner cannot perform reads at a read timestamp more than one hour in the past.
So any export would need to complete within an hour. A single reader (i.e. select * from table; using timestamp X) would not be able to export the entire table within an hour.
How can multiple parallel readers be implemented in spanner?
Note: It is mentioned in one of the comments that support for Apache Beam is coming, but it looks like that uses a single reader:
/** A simplest read function implementation. Parallelism support is coming. */
https://github.com/apache/beam/blob/master/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/spanner/NaiveSpannerReadFn.java#L26
Is there a way to do the parallel reader that beam requires today using exising APIs? Or will Beam need to use something that isn't released yet on google spanner?
It is possible to read data in parallel from Cloud Spanner with the BatchClient class. Follow read_data_in_parallel for more information.
If you are looking to export data from Cloud Spanner, I'd recommend you to use Cloud Dataflow (see the integration details here) as it provides higher level abstractions and takes care data processing details, like scaling and failure handling.
Edit 2018-03-30 - The example project has been updated to use the BatchClient offered by Google Cloud Spanner
After the release of the BatchClient for reading/downloading large amounts of data, the example project below has been updated to use the new batch client instead of the standard database client. The basic idea behind the project is still the same: Copy data to/from Cloud Spanner and any other database using standard jdbc functionality. The following code snippet sets the jdbc connection in batch read mode:
if (source.isWrapperFor(ICloudSpannerConnection.class))
{
ICloudSpannerConnection con = source.unwrap(ICloudSpannerConnection.class);
// Make sure no transaction is running
if (!con.isBatchReadOnly())
{
if (con.getAutoCommit())
{
con.setAutoCommit(false);
}
else
{
con.commit();
}
con.setBatchReadOnly(true);
}
}
When the connection is in 'batch read only mode', the connection will use the BatchClient of Google Cloud Spanner instead of the standard database client. When one of the Statement#execute(String) or PreparedStatement#execute() methods are called (as these allow multiple result sets to be returned) the jdbc driver will create a partitioned query instead of a normal query. The results of this partitioned query will be a number of result sets (one per partition) that can be fetched by the Statement#getResultSet() and Statement#getMoreResults(int) methods.
Statement statement = source.createStatement();
boolean hasResults = statement.execute(select);
int workerNumber = 0;
while (hasResults)
{
ResultSet rs = statement.getResultSet();
PartitionWorker worker = new PartitionWorker("PartionWorker-" + workerNumber, config, rs, tableSpec, table, insertCols);
workers.add(worker);
hasResults = statement.getMoreResults(Statement.KEEP_CURRENT_RESULT);
workerNumber++;
}
The result sets that are returned by the Statement#execute(String) are not executed directly, but only after the first call to ResultSet#next(). Passing these result sets to separate worker threads ensures parallel download and copying of the data.
Original answer:
This project was initially created for conversion in the other direction (from a local database to Cloud Spanner), but as it uses JDBC for both source and destination it can also be used the other way around: Converting a Cloud Spanner database to a local PostgreSQL database. Large tables are converted in parallel using a thread pool.
The project uses this open source JDBC driver instead of the JDBC driver supplied by Google. The source Cloud Spanner JDBC connection is set to read-only mode and autocommit=false. This ensures that the connection automatically creates a read-only transaction using the current time as timestamp the first time you execute a query. All subsequent queries within the same (read-only) transaction will use the same timestamp giving you a consistent snapshot of your Google Cloud Spanner database.
It works as follows:
Set the source database to read-only transactional mode.
The convert(String catalog, String schema) method iterates over all
tables in the source database (Cloud Spanner)
For each table the number of records is determined, and depending on the size of the table, the table is copied using either the main thread of the application or by a worker pool.
The class UploadWorker is responsible for the parallel copying. Each worker is assigned a range of records from the table (for example rows 1 to 2,400). The range is selected by a select statement in this format: 'SELECT * FROM $TABLE ORDER BY $PK_COLUMNS LIMIT $BATCH_SIZE OFFSET $CURRENT_OFFSET'
Commit the read-only transaction on the source database after ALL tables have been converted.
Below is a code snippet of the most important parts.
public void convert(String catalog, String schema) throws SQLException
{
int batchSize = config.getBatchSize();
destination.setAutoCommit(false);
// Set the source connection to transaction mode (no autocommit) and read-only
source.setAutoCommit(false);
source.setReadOnly(true);
try (ResultSet tables = destination.getMetaData().getTables(catalog, schema, null, new String[] { "TABLE" }))
{
while (tables.next())
{
String tableSchema = tables.getString("TABLE_SCHEM");
if (!config.getDestinationDatabaseType().isSystemSchema(tableSchema))
{
String table = tables.getString("TABLE_NAME");
// Check whether the destination table is empty.
int destinationRecordCount = getDestinationRecordCount(table);
if (destinationRecordCount == 0 || config.getDataConvertMode() == ConvertMode.DropAndRecreate)
{
if (destinationRecordCount > 0)
{
deleteAll(table);
}
int sourceRecordCount = getSourceRecordCount(getTableSpec(catalog, tableSchema, table));
if (sourceRecordCount > batchSize)
{
convertTableWithWorkers(catalog, tableSchema, table);
}
else
{
convertTable(catalog, tableSchema, table);
}
}
else
{
if (config.getDataConvertMode() == ConvertMode.ThrowExceptionIfExists)
throw new IllegalStateException("Table " + table + " is not empty");
else if (config.getDataConvertMode() == ConvertMode.SkipExisting)
log.info("Skipping data copy for table " + table);
}
}
}
}
source.commit();
}
private void convertTableWithWorkers(String catalog, String schema, String table) throws SQLException
{
String tableSpec = getTableSpec(catalog, schema, table);
Columns insertCols = getColumns(catalog, schema, table, false);
Columns selectCols = getColumns(catalog, schema, table, true);
if (insertCols.primaryKeyCols.isEmpty())
{
log.warning("Table " + tableSpec + " does not have a primary key. No data will be copied.");
return;
}
log.info("About to copy data from table " + tableSpec);
int batchSize = config.getBatchSize();
int totalRecordCount = getSourceRecordCount(tableSpec);
int numberOfWorkers = calculateNumberOfWorkers(totalRecordCount);
int numberOfRecordsPerWorker = totalRecordCount / numberOfWorkers;
if (totalRecordCount % numberOfWorkers > 0)
numberOfRecordsPerWorker++;
int currentOffset = 0;
ExecutorService service = Executors.newFixedThreadPool(numberOfWorkers);
for (int workerNumber = 0; workerNumber < numberOfWorkers; workerNumber++)
{
int workerRecordCount = Math.min(numberOfRecordsPerWorker, totalRecordCount - currentOffset);
UploadWorker worker = new UploadWorker("UploadWorker-" + workerNumber, selectFormat, tableSpec, table,
insertCols, selectCols, currentOffset, workerRecordCount, batchSize, source,
config.getUrlDestination(), config.isUseJdbcBatching());
service.submit(worker);
currentOffset = currentOffset + numberOfRecordsPerWorker;
}
service.shutdown();
try
{
service.awaitTermination(config.getUploadWorkerMaxWaitInMinutes(), TimeUnit.MINUTES);
}
catch (InterruptedException e)
{
log.severe("Error while waiting for workers to finish: " + e.getMessage());
throw new RuntimeException(e);
}
}
public class UploadWorker implements Runnable
{
private static final Logger log = Logger.getLogger(UploadWorker.class.getName());
private final String name;
private String selectFormat;
private String sourceTable;
private String destinationTable;
private Columns insertCols;
private Columns selectCols;
private int beginOffset;
private int numberOfRecordsToCopy;
private int batchSize;
private Connection source;
private String urlDestination;
private boolean useJdbcBatching;
UploadWorker(String name, String selectFormat, String sourceTable, String destinationTable, Columns insertCols,
Columns selectCols, int beginOffset, int numberOfRecordsToCopy, int batchSize, Connection source,
String urlDestination, boolean useJdbcBatching)
{
this.name = name;
this.selectFormat = selectFormat;
this.sourceTable = sourceTable;
this.destinationTable = destinationTable;
this.insertCols = insertCols;
this.selectCols = selectCols;
this.beginOffset = beginOffset;
this.numberOfRecordsToCopy = numberOfRecordsToCopy;
this.batchSize = batchSize;
this.source = source;
this.urlDestination = urlDestination;
this.useJdbcBatching = useJdbcBatching;
}
#Override
public void run()
{
// Connection source = DriverManager.getConnection(urlSource);
try (Connection destination = DriverManager.getConnection(urlDestination))
{
log.info(name + ": " + sourceTable + ": Starting copying " + numberOfRecordsToCopy + " records");
destination.setAutoCommit(false);
String sql = "INSERT INTO " + destinationTable + " (" + insertCols.getColumnNames() + ") VALUES \n";
sql = sql + "(" + insertCols.getColumnParameters() + ")";
PreparedStatement statement = destination.prepareStatement(sql);
int lastRecord = beginOffset + numberOfRecordsToCopy;
int recordCount = 0;
int currentOffset = beginOffset;
while (true)
{
int limit = Math.min(batchSize, lastRecord - currentOffset);
String select = selectFormat.replace("$COLUMNS", selectCols.getColumnNames());
select = select.replace("$TABLE", sourceTable);
select = select.replace("$PRIMARY_KEY", selectCols.getPrimaryKeyColumns());
select = select.replace("$BATCH_SIZE", String.valueOf(limit));
select = select.replace("$OFFSET", String.valueOf(currentOffset));
try (ResultSet rs = source.createStatement().executeQuery(select))
{
while (rs.next())
{
int index = 1;
for (Integer type : insertCols.columnTypes)
{
Object object = rs.getObject(index);
statement.setObject(index, object, type);
index++;
}
if (useJdbcBatching)
statement.addBatch();
else
statement.executeUpdate();
recordCount++;
}
if (useJdbcBatching)
statement.executeBatch();
}
destination.commit();
log.info(name + ": " + sourceTable + ": Records copied so far: " + recordCount + " of "
+ numberOfRecordsToCopy);
currentOffset = currentOffset + batchSize;
if (recordCount >= numberOfRecordsToCopy)
break;
}
}
catch (SQLException e)
{
log.severe("Error during data copy: " + e.getMessage());
throw new RuntimeException(e);
}
log.info(name + ": Finished copying");
}
}

Cassandra Map Reduce for TimeUUID columns

I recently Setup 4 node Cassandra cluster for learning with one column family which hold time series data as.
Key -> {column name: timeUUID, column value: csv log line, ttl: 1year}, I use Netflix Astyanax java client to load about 1 million log lines.
I also configured Hadoop to run map-reduce jobs with 1 namenode and 4 datanode's to run some analytics on Cassandra data.
All the available examples on internet uses column name as SlicePredicate for Hadoop Job Configuration, where as I have timeUUID as columns how can I efficiently feed Cassandra data to Hadoop Job configurator with batches of 1000 columns at one time.
There are more than 10000 column's for some rows in this test data and expected to be more in real data.
I configure my job as
public int run(String[] arg0) throws Exception {
Job job = new Job(getConf(), JOB_NAME);
Job.setJarByClass(LogTypeCounterByDate.class);
job.setMapperClass(LogTypeCounterByDateMapper.class);
job.setReducerClass(LogTypeCounterByDateReducer.class);
job.setInputFormatClass(ColumnFamilyInputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
ConfigHelper.setRangeBatchSize(getConf(), 1000);
SliceRange sliceRange = new SliceRange(ByteBuffer.wrap(new byte[0]),
ByteBuffer.wrap(new byte[0]), true, 1000);
SlicePredicate slicePredicate = new SlicePredicate();
slicePredicate.setSlice_range(sliceRange);
ConfigHelper.setInputColumnFamily(job.getConfiguration(), KEYSPACE, COLUMN_FAMILY);
ConfigHelper.setInputRpcPort(job.getConfiguration(), INPUT_RPC_PORT);
ConfigHelper.setInputInitialAddress(job.getConfiguration(), INPUT_INITIAL_ADRESS);
ConfigHelper.setInputPartitioner(job.getConfiguration(), INPUT_PARTITIONER);
ConfigHelper.setInputSlicePredicate(job.getConfiguration(), slicePredicate);
FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));
job.waitForCompletion(true);
return job.isSuccessful() ? 0 : 1;
}
But I can't able to understand how I define Mapper, kindly can you provide template for Mapper class.
public static class LogTypeCounterByDateMapper extends Mapper<ByteBuffer, SortedMap<ByteBuffer, IColumn>, Text, LongWritable>
{
private Text key = null;
private LongWritable value = null;
#Override
protected void setup(Context context){
}
public void map(ByteBuffer key, SortedMap<ByteBuffer, IColumn> columns, Context context){
//String[] lines = columns.;
}
}
ConfigHelper.setRangeBatchSize(getConf(), 1000)
...
SlicePredicate predicate = new SlicePredicate().setSlice_range(new SliceRange(TimeUUID.asByteBuffer(startValue), TimeUUID.asByteBuffer(endValue), false, 1000))
ConfigHelper.setInputSlicePredicate(conf, predicate)

T4 Templates : Reading resulting columns of a stored procedure table

I am learning T4 templates right now, and all examples I got on internet is about using the tables for code generation. I want to use stored procedure result columns to generate automated UI, is it possible? OR I have to create view for same query? in that case, how to read from view?
Thanks in advance.
I got the solution and here is how you can generate a rad grid directly from the sp name
<#
'requires: <## assembly name="System.Data" #>
dim Server as new Server(".\sqlexpress")
dim database as new Database(server, "xxxx")
dim strSpName as String= "sp_xxxx"
Dim dt as System.Data.DataTable= database.ExecuteWithResults("exec sp_GetEquipment").Tables(0)
dim ctlName as String = "grdEqp"
#>
<telerik:RadGrid ID="grd" runat="server" Skin="Web20" AutoGenerateColumns="false">
<MasterTableView>
<Columns>
<#
For Each column As System.Data.DataColumn In dt.Columns
#><telerik:GridBoundColumn DataField="<#=column.ColumnName #>" HeaderText="<#=column.ColumnName #>"/>
<#Next#>
</Columns>
</MasterTableView>
</telerik:RadGrid>
If you don't actually want to execute the stored procedure as various stored procedures have a number of different parameters passed then you could use the sp_describe_first_result_set system stored procedure to return the columns of the result set assuming there is just one.
/// <summary>
/// Returns table for which stored procedures need to be generated.
/// </summary>
string TableName = "usp_getNominalCode";
string SchemaName = "Financial";
DataTable DataTable
{
get
{
if (_table == null)
{
Server server = new Server(new ServerConnection(new SqlConnection(this.ConnectionString)));
SqlConnectionStringBuilder connectionStringBuilder = new SqlConnectionStringBuilder(this.ConnectionString);
Database database = new Database(server, connectionStringBuilder.InitialCatalog);
DataSet storedProcedureColumns = database.ExecuteWithResults("sp_describe_first_result_set #tsql= " + "'[" + SchemaName + "]" + ".[" + TableName + "]'");
_table = storedProcedureColumns.Tables[0];
}
return _table;
}
}
DataTable _table;
You can then query this table for it's structure like the other answer but it'll be a little more generic