Openrowset sql server là gì bị lỗi 65001 năm 2024
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While running the below script in sqlproj using Visual Studio 17.9 preview 1, I am getting the error 'Incorrect syntax near '65001' while the same query runs successfully in SSMS 19. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. How to use OPENROWSET using serverless SQL pool in Azure Synapse Analytics
In this articleThe 2 function allows you to access files in Azure Storage. 3 function reads content of a remote data source (for example file) and returns the content as a set of rows. Within the serverless SQL pool resource, the OPENROWSET bulk rowset provider is accessed by calling the OPENROWSET function and specifying the BULK option. The 3 function can be referenced in the 5 clause of a query as if it were a table name 3. It supports bulk operations through a built-in BULK provider that enables data from a file to be read and returned as a rowset. Note The OPENROWSET function is not supported in dedicated SQL pool. Data sourceOPENROWSET function in Synapse SQL reads the content of the file(s) from a data source. The data source is an Azure storage account and it can be explicitly referenced in the 3 function or can be dynamically inferred from URL of the files that you want to read. The 3 function can optionally contain a 9 parameter to specify the data source that contains files.
This is a quick and easy way to read the content of the files without pre-configuration. This option enables you to use the basic authentication option to access the storage (Microsoft Entra passthrough for Microsoft Entra logins and SAS token for SQL logins).
SecurityA database user must have 8 permission to use the 3 function. The storage administrator must also enable a user to access the files by providing valid SAS token or enabling Microsoft Entra principal to access storage files. Learn more about storage access control in this article. 3 use the following rules to determine how to authenticate to storage:
Caller must have 2 permission on credential to use it to authenticate to storage. SyntaxArgumentsYou have three choices for input files that contain the target data for querying. Valid values are:
Values with blank spaces are not valid, e.g. 'CSV ' is not a valid value. 'unstructured_data_path' The unstructured_data_path that establishes a path to the data may be an absolute or relative path:
Below you'll find the relevant External Data Source Prefix Storage account path Azure Blob Storage http[s] ' Specifies a path within your storage that points to the folder or file you want to read. If the path points to a container or folder, all files will be read from that particular container or folder. Files in subfolders won't be included. You can use wildcards to target multiple files or folders. Usage of multiple nonconsecutive wildcards is allowed. Below is an example that reads all csv files starting with population from all folders starting with /csv/population: 7 If you specify the unstructured_data_path to be a folder, a serverless SQL pool query will retrieve files from that folder. You can instruct serverless SQL pool to traverse folders by specifying /* at the end of path as in example: 8 Note Unlike Hadoop and PolyBase, serverless SQL pool doesn't return subfolders unless you specify /** at the end of path. Just like Hadoop and PolyBase, it doesn't return files for which the file name begins with an underline (_) or a period (.). In the example below, if the unstructured_data_path= 9, a serverless SQL pool query will return rows from mydata.txt. It won't return mydata2.txt and mydata3.txt because they're located in a subfolder. 0 The WITH clause allows you to specify columns that you want to read from files.
column_name = Name for the output column. If provided, this name overrides the column name in the source file and column name provided in JSON path if there is one. If json_path is not provided, it will be automatically added as '$.column_name'. Check json_path argument for behavior. column_type = Data type for the output column. The implicit data type conversion will take place here. column_ordinal = Ordinal number of the column in the source file(s). This argument is ignored for Parquet files since binding is done by name. The following example would return a second column only from a CSV file: json_path = JSON path expression to column or nested property. Default is lax. Note In strict mode query will fail with error if provided path does not exist. In lax mode query will succeed and JSON path expression will evaluate to NULL. FIELDTERMINATOR ='field_terminator' Specifies the field terminator to be used. The default field terminator is a comma (","). ROWTERMINATOR ='row_terminator'` Specifies the row terminator to be used. If row terminator is not specified, one of default terminators will be used. Default terminators for PARSER_VERSION = '1.0' are \r\n, \n and \r. Default terminators for PARSER_VERSION = '2.0' are \r\n and \n. Note When you use PARSER_VERSION='1.0' and specify \n (newline) as the row terminator, it will be automatically prefixed with a \r (carriage return) character, which results in a row terminator of \r\n. ESCAPE_CHAR = 'char' Specifies the character in the file that is used to escape itself and all delimiter values in the file. If the escape character is followed by a value other than itself, or any of the delimiter values, the escape character is dropped when reading the value. The ESCAPECHAR parameter will be applied regardless of whether the FIELDQUOTE is or isn't enabled. It won't be used to escape the quoting character. The quoting character must be escaped with another quoting character. Quoting character can appear within column value only if value is encapsulated with quoting characters. FIRSTROW = 'first_row' Specifies the number of the first row to load. The default is 1 and indicates the first row in the specified data file. The row numbers are determined by counting the row terminators. FIRSTROW is 1-based. FIELDQUOTE = 'field_quote' Specifies a character that will be used as the quote character in the CSV file. If not specified, the quote character (") will be used. DATA_COMPRESSION = 'data_compression_method' Specifies compression method. Supported in PARSER_VERSION='1.0' only. Following compression method is supported:
PARSER_VERSION = 'parser_version' Specifies parser version to be used when reading files. Currently supported CSV parser versions are 1.0 and 2.0:
CSV parser version 1.0 is default and feature rich. Version 2.0 is built for performance and does not support all options and encodings. CSV parser version 1.0 specifics:
CSV parser version 2.0 specifics:
HEADER_ROW = { TRUE | FALSE } Specifies whether a CSV file contains header row. Default is 2 Supported in PARSER_VERSION='2.0'. If TRUE, the column names will be read from the first row according to FIRSTROW argument. If TRUE and schema is specified using WITH, binding of column names will be done by column name, not ordinal positions. DATAFILETYPE = { 'char' | 'widechar' } Specifies encoding: 3 is used for UTF8, 4 is used for UTF16 files. CODEPAGE = { 'ACP' | 'OEM' | 'RAW' | 'code_page' } Specifies the code page of the data in the data file. The default value is 65001 (UTF-8 encoding). See more details about this option . ROWSET_OPTIONS = '{"READ_OPTIONS":["ALLOW_INCONSISTENT_READS"]}' This option will disable the file modification check during the query execution, and read the files that are updated while the query is running. This is useful option when you need to read append-only files that are appended while the query is running. In the appendable files, the existing content is not updated, and only new rows are added. Therefore, the probability of wrong results is minimized compared to the updateable files. This option might enable you to read the frequently appended files without handling the errors. See more information in section. Reject Options Note Rejected rows feature is in Public Preview. Please note that rejected rows feature works for delimited text files and PARSER_VERSION 1.0. You can specify reject parameters that determine how service will handle dirty records it retrieves from the external data source. A data record is considered 'dirty' if actual data types don't match the column definitions of the external table. When you don't specify or change reject options, service uses default values. Service will use the reject options to determine the number of rows that can be rejected before the actual query fails. The query will return (partial) results until the reject threshold is exceeded. It then fails with the appropriate error message. MAXERRORS = reject_value Specifies the number of rows that can be rejected before the query fails. MAXERRORS must be an integer between 0 and 2,147,483,647. ERRORFILE_DATA_SOURCE = data source Specifies data source where rejected rows and the corresponding error file should be written. ERRORFILE_LOCATION = Directory Location Specifies the directory within the DATA_SOURCE, or ERROR_FILE_DATASOURCE if specified, that the rejected rows and the corresponding error file should be written. If the specified path doesn't exist, service will create one on your behalf. A child directory is created with the name "rejectedrows". The "" character ensures that the directory is escaped for other data processing unless explicitly named in the location parameter. Within this directory, there's a folder created based on the time of load submission in the format YearMonthDay_HourMinuteSecond_StatementID (Ex. 20180330-173205-559EE7D2-196D-400A-806D-3BF5D007F891). You can use statement id to correlate folder with query that generated it. In this folder, two files are written: error.json file and the data file. error.json file contains json array with encountered errors related to rejected rows. Each element representing error contains following attributes: Attribute Description Error Reason why row is rejected. Row Rejected row ordinal number in file. Column Rejected column ordinal number. Value Rejected column value. If the value is larger than 100 characters, only the first 100 characters will be displayed. File Path to file that row belongs to. Fast delimited text parsingThere are two delimited text parser versions you can use. CSV parser version 1.0 is default and feature rich while parser version 2.0 is built for performance. Performance improvement in parser 2.0 comes from advanced parsing techniques and multi-threading. Difference in speed will be bigger as the file size grows. Automatic schema discoveryYou can easily query both CSV and Parquet files without knowing or specifying schema by omitting WITH clause. Column names and data types will be inferred from files. Parquet files contain column metadata, which will be read, type mappings can be found in . Check for samples. For the CSV files, column names can be read from header row. You can specify whether header row exists using HEADER_ROW argument. If HEADER_ROW = FALSE, generic column names will be used: C1, C2, ... Cn where n is number of columns in file. Data types will be inferred from first 100 data rows. Check for samples. Have in mind that if you are reading number of files at once, the schema will be inferred from the first file service gets from the storage. This can mean that some of the columns expected are omitted, all because the file used by the service to define the schema did not contain these columns. In that case, please use OPENROWSET WITH clause. Important There are cases when appropriate data type cannot be inferred due to lack of information and larger data type will be used instead. This brings performance overhead and is particularly important for character columns which will be inferred as varchar(8000). For optimal performance, please and . Type mapping for ParquetParquet and Delta Lake files contain type descriptions for every column. The following table describes how Parquet types are mapped to SQL native types. Parquet type Parquet logical type (annotation) SQL data type BOOLEAN bit BINARY / BYTE_ARRAY varbinary DOUBLE float FLOAT real INT32 int INT64 bigint INT96 datetime2 FIXED_LEN_BYTE_ARRAY binary BINARY UTF8 varchar *(UTF8 collation) BINARY STRING varchar *(UTF8 collation) BINARY ENUM varchar *(UTF8 collation) FIXED_LEN_BYTE_ARRAY UUID uniqueidentifier BINARY DECIMAL decimal BINARY JSON varchar(8000) *(UTF8 collation) BINARY BSON Not supported FIXED_LEN_BYTE_ARRAY DECIMAL decimal BYTE_ARRAY INTERVAL Not supported INT32 INT(8, true) smallint INT32 INT(16, true) smallint INT32 INT(32, true) int INT32 INT(8, false) tinyint INT32 INT(16, false) int INT32 INT(32, false) bigint INT32 DATE date INT32 DECIMAL decimal INT32 TIME (MILLIS) time INT64 INT(64, true) bigint INT64 INT(64, false) decimal(20,0) INT64 DECIMAL decimal INT64 TIME (MICROS) time INT64 TIME (NANOS) Not supported INT64 TIMESTAMP () (MILLIS / MICROS) datetime2 INT64 TIMESTAMP () (MILLIS / MICROS) bigint - make sure that you explicitly adjust 5 value with the timezone offset before converting it to a datetime value. INT64 TIMESTAMP (NANOS) Not supported LIST varchar(8000), serialized into JSON MAP varchar(8000), serialized into JSON ExamplesRead CSV files without specifying schemaThe following example reads CSV file that contains header row without specifying column names and data types: The following example reads CSV file that doesn't contain header row without specifying column names and data types: Read Parquet files without specifying schemaThe following example returns all columns of the first row from the census data set, in Parquet format, and without specifying column names and data types: Read Delta Lake files without specifying schemaThe following example returns all columns of the first row from the census data set, in Delta Lake format, and without specifying column names and data types: Read specific columns from CSV fileThe following example returns only two columns with ordinal numbers 1 and 4 from the population*.csv files. Since there's no header row in the files, it starts reading from the first line: Read specific columns from Parquet fileThe following example returns only two columns of the first row from the census data set, in Parquet format: Specify columns using JSON pathsThe following example shows how you can use JSON path expressions in WITH clause and demonstrates difference between strict and lax path modes: 0 Specify multiple files/folders in BULK pathThe following example shows how you can use multiple file/folder paths in BULK parameter: 1 Next stepsFor more samples, see the query data storage quickstart to learn how to use 3 to read CSV, PARQUET, DELTA LAKE, and JSON file formats. Check best practices for achieving optimal performance. You can also learn how to save the results of your query to Azure Storage using CETAS. |
