An exception is thrown if there is ambiguity, i.e. more than one Parquet column is matched. This change also applies to Parquet Hive tables when spark.sql.hive.convertMetastoreParquet is set to true. The row and column delimiters may be any String , not just a single character. The export function is more general than just a table data exporter. Besides the trivial generalization that you may specify a view or other virtual table name in place of a table name, you can alternatively export the output of any query which produces normal text output. (This could actually even be multiple multiple-line SQL statements, as long as the last one outputs the needed data cells).
When you Execute SqlTool with a SQL script, it also behaves by default exactly as you would want it to. If any error is encountered, the connection will be rolled back, then SqlTool will exit with an error exit value. If you wish, you can detect and handle error conditions yourself. For scripts expected to produce errors , you can have SqlTool continue-upon-error. For SQL script-writers, you will have access to portable scripting features which you've had to live without until now. You can use variables set on the command line or in your script.
You can handle specific errors based on the output of SQL commands or of your variables. You can chain SQL scripts, invoke external programs, dump data to files, use prepared statements, Finally, you have a procedural language with if, foreach, while, continue, and break statements. Note that the import command will not create a new table. This is because of the impossibility of guessing appropriate types and constraints based only on column names and a data sampling (which is all that a DSV-importer has access to). Therefore, if you wish to populate a new table, create the table before running the import.
The import file does not need to have data for all columns of a table. Since Spark 2.2.1 and 2.3.0, the schema is always inferred at runtime when the data source tables have the columns that exist in both partition schema and data schema. The inferred schema does not have the partitioned columns. When reading the table, Spark respects the partition values of these overlapping columns instead of the values stored in the data source files.
In 2.2.0 and 2.1.x release, the inferred schema is partitioned but the data of the table is invisible to users (i.e., the result set is empty). In Spark version 2.4 and below, you can create a map with duplicated keys via built-in functions like CreateMap, StringToMap, etc. In Spark 3.0, Spark throws RuntimeException when duplicated keys are found. You can set spark.sql.mapKeyDedupPolicy to LAST_WIN to deduplicate map keys with last wins policy.
Users may still read map values with duplicated keys from data sources which do not enforce it , the behavior is undefined. When calling stored procedures, always use parameter markers for argument markers instead of using literal arguments. When you execute a stored procedure as a SQL query, the database server parses the statement, validates the argument types, and converts the arguments into the correct data types. There is no need to use the CallableStatement object with this type of stored procedure; you can use a simple JDBC statement. In Spark version 2.4 and below, the parser of JSON data source treats empty strings as null for some data types such as IntegerType.
For FloatType, DoubleType, DateType and TimestampType, it fails on empty strings and throws exceptions. Spark 3.0 disallows empty strings and will throw an exception for data types except for StringType and BinaryType. The previous behavior of allowing an empty string can be restored by setting spark.sql.legacy.json.allowEmptyString.enabled to true. In Spark 3.2, create/alter view will fail if the input query output columns contain auto-generated alias.
This is necessary to make sure the query output column names are stable across different spark versions. To restore the behavior before Spark 3.2, set spark.sql.legacy.allowAutoGeneratedAliasForView to true. The available parameters are determined automatically from the driver, and may change from version to version. They are usually not required, since sensible defaults are assumed.ValueA value for the given parameter.
Parameters and their allowed values are somewhat database-specific. The links below may help, or if you upload your own JDBC Driver, consult the documentation that was provided with it. Spring Data query methods usually return one or multiple instances of the aggregate root managed by the repository. However, it might sometimes be desirable to create projections based on certain attributes of those types. Spring Data allows modeling dedicated return types, to more selectively retrieve partial views of the managed aggregates. The overhead for the initial execution of a PreparedStatement object is high.
The benefit comes with subsequent executions of the SQL statement. For example, suppose we are preparing and executing a query that returns employee information based on an ID. Using a PreparedStatement object, a JDBC driver would process the prepare request by making a network request to the database server to parse and optimize the query.
If the application will only make this request once during its lifespan, using a Statement object instead of a PreparedStatement object results in only a single network roundtrip to the database server. Reducing network communication typically provides the most performance gains. When created once, they can be called by any database client, such as JDBC applications, as many times as it wants without the need for a new execution plan.
In version 2.3 and earlier, CSV rows are considered as malformed if at least one column value in the row is malformed. CSV parser dropped such rows in the DROPMALFORMED mode or outputs an error in the FAILFAST mode. Since Spark 2.4, CSV row is considered as malformed only when it contains malformed column values requested from CSV datasource, other values can be ignored. As an example, CSV file contains the "id,name" header and one row "1234". In Spark 2.4, selection of the id column consists of a row with one column value 1234 but in Spark 2.3 and earlier it is empty in the DROPMALFORMED mode.
To restore the previous behavior, set spark.sql.csv.parser.columnPruning.enabled to false. In Spark 3.0, when inserting a value into a table column with a different data type, the type coercion is performed as per ANSI SQL standard. Certain unreasonable type conversions such as converting string to int and double to boolean are disallowed. A runtime exception is thrown if the value is out-of-range for the data type of the column. In Spark version 2.4 and below, type conversions during table insertion are allowed as long as they are valid Cast. When inserting an out-of-range value to an integral field, the low-order bits of the value is inserted(the same as Java/Scala numeric type casting).
For example, if 257 is inserted to a field of byte type, the result is 1. The behavior is controlled by the option spark.sql.storeAssignmentPolicy, with a default value as "ANSI". Setting the option as "Legacy" restores the previous behavior. Since Spark 3.1, CHAR/CHARACTER and VARCHAR types are supported in the table schema. Table scan/insertion will respect the char/varchar semantic.
If char/varchar is used in places other than table schema, an exception will be thrown (CAST is an exception that simply treats char/varchar as string like before). To restore the behavior before Spark 3.1, which treats them as STRING types and ignores a length parameter, e.g. CHAR, you can set spark.sql.legacy.charVarcharAsString to true. By default, Livy has a limit of 1,000 rows on the result set of a query. It is not ideal to increase this limit since the result set is stored in memory, and increasing this limitation can lead to issues at scale in a memory restrictive environment like ours.
To solve this problem, we implemented AWS S3 redirection for the final result of each query. This way, large result sets can be uploaded to S3 in a multi-part fashion without impacting the overall performance of the service. On the client, we later retrieve the final S3 output path returned in the REST response and fetch the results from S3 in a paginated fashion.
This makes the retrieval faster without running the risk of S3 timeouts while listing the path objects. You can however not add operators operating on queries using database functions. The Slick Scala-to-SQL compiler requires knowledge about the structure of the query in order to compile it to the most simple SQL query it can produce.
It currently couldn't handle custom query operators in that context. An example for such operator is a MySQL index hint, which is not supported by Slick's type-safe api and it cannot be added by users. Although almost no JDBC application can be written without database metadata methods, you can improve system performance by minimizing their use. These particular elements of the SQL language are performance expensive. Next, we'll demonstrate how to call a stored procedure that returns one or more OUT parameters.
These are the parameters that the stored procedure uses to return data to the calling application as single values, not as a result set as we saw earlier. The SQL syntax used for IN/OUT stored procedures is similar to what we showed earlier. Due to wildly varying support and behavior of data and time types in SQL databases, SqlTool always converts date-type and time-type values being imported from DSV files using java.sql.Timestamps. This usually provides more resolution than is needed, but is required for portability.
Therefore, questions about acceptable date/time formats are ultimately decided by the Java's java.sql.Timestamp class. Slick comes with a Scala-to-SQL compiler, which allows a sub-set of the Scala language to be compiled to SQL queries. Also available are a subset of the standard library and some extensions, e.g. for joins.
The fact that such queries are type-safe not only catches many mistakes early at compile time, but also eliminates the risk of SQL injection vulnerabilities. When implemented correctly, stored procedures produce the same results as prepared statements. 1The identifier property is final but set to null in the constructor. The class exposes a withId(…) method that's used to set the identifier, e.g. when an instance is inserted into the datastore and an identifier has been generated. The original Person instance stays unchanged as a new one is created.
The same pattern is usually applied for other properties that are store managed but might have to be changed for persistence operations. With the design shown, the database value will trump the defaulting as Spring Data uses the only declared constructor. The core idea here is to use factory methods instead of additional constructors to avoid the need for constructor disambiguation through @PersistenceConstructor. Instead, defaulting of properties is handled within the factory method. 1PropertyAccessor's hold a mutable instance of the underlying object.
This is, to enable mutations of otherwise immutable properties.2By default, Spring Data uses field-access to read and write property values. All subsequent mutations will take place in the new instance leaving the previous untouched.4Using property-access allows direct method invocations without using MethodHandles. The use of prepared statements with variable binding is how all developers should first be taught how to write database queries. They are simple to write, and easier to understand than dynamic queries. Parameterized queries force the developer to first define all the SQL code, and then pass in each parameter to the query later. This coding style allows the database to distinguish between code and data, regardless of what user input is supplied.
Although programmatic updates do not apply to all types of applications, developers should attempt to use programmatic updates and deletes. Using the updateXXX methods of the ResultSet object allows the developer to update data without building a complex SQL statement. Instead, the developer simply supplies the column in the result set that is to be updated and the data that is to be changed. Then, before moving the cursor from the row in the result set, the updateRow method must be called to update the database as well. Many databases have hidden columns (called pseudo-columns) that represent a unique key over every row in a table. Typically, using these types of columns in a query is the fastest way to access a row because the pseudo-columns usually represent the physical disk address of the data.
Prior to JDBC 3.0, an application could only retrieve the value of the pseudo-columns by executing a Select statement immediately after inserting the data. The above code uses %s placeholders to insert the received input in the update_query string. For the first time in this tutorial, you have multiple queries inside a single string. To pass multiple queries to a single cursor.execute(), you need to set the method's multi argument to True.
The Informix JDBC driver provides the Statement, PreparedStatement, and CallableStatement methods, which can be used to execute stored procedures. Which method you use depends on the characteristic of the stored procedure. For example, if the stored procedure returns a single value, you should use a JDBC Statement object.
The following table provides some guidelines for what method to use for which stored procedure type. Suppose we have a JDBC application that needs to efficiently repeat a sequence of tasks again and again. We might think of using a Java™ method, but how many times do we want to do client/server communication to send and receive data? The database server will prepare and generate a query plan for every SQL statement sent by the application, which will consume some CPU time. While taking performance into consideration, using simple Java methods with single SQL statements may be a bad idea. How to use stored procedures varies from one database server to another.
A Database Management System such as Informix and DB2®, have different SQL syntax to execute stored procedures. This makes things difficult for application developers when they need to write code targeted to several DBMSes. A callable statement provides a method to execute stored procedures using the same SQL syntax in all DBMS systems. You can upload binary files such as photographs, audio files, or serialized Java objects into database columns.
SqlTool keeps one binary buffer which you can load from files with the \bl command, or from a database query by doing a one-row query for any non-displayable type . In the latter case, the data returned for the first non-displayable column of the first result row will be stored into the binary buffer. This is an inconvenience, since the database engine will change names in SQL to default case unless you double-quote the name, but that is server-side functionality which cannot be reproduced by SqlTool. You can use spaces and other special characters in the string. JSON data source will not automatically load new files that are created by other applications (i.e. files that are not inserted to the dataset through Spark SQL).
For a DataFrame representing a JSON dataset, users need to recreate the DataFrame and the new DataFrame will include new files. Since Spark 2.3, when all inputs are binary, functions.concat() returns an output as binary. Until Spark 2.3, it always returns as a string despite of input types. To keep the old behavior, set spark.sql.function.concatBinaryAsString to true. In Spark 3.0, when the array/map function is called without any parameters, it returns an empty collection with NullType as element type.
