一、当后缀名为zip、gzip，spark可以自动处理和读取
1、spark非常智能，如果一批压缩的zip和gzip文件，并且里面为一堆text文件时，可以用如下方式读取或者获取读取后的schema
spark.read.text("xxxxxxxx/xxxx.zip")
spark.read.text("xxxxxxxx/xxxx.zip").schema
spark.read.text("xxxxxxxx/xxxx.gz")
spark.read.text("xxxxxxxx/xxxx.gz").schema
2、当压缩的一批text文件里面的内容为json时，还可以通过read.json读取，并且自动解析为json数据返回
spark.read.json("xxxxxxxx/xxxx.zip")
spark.read.json("xxxxxxxx/xxxx.zip").schema
spark.read.json("xxxxxxxx/xxxx.gz")
spark.read.json("xxxxxxxx/xxxx.gz").schema
备注：spark在读取text、zip、gzip等各种文件时，支持直接传入类似这样的通配符匹配路径 转载请注明：张永清 博客园：https://www.cnblogs.com/laoqing/p/15516458.html
spark.read.text("xxxxxxxx/*.zip")
spark.read.text("xxxxxxxx/*")
spark读取文件内容时是按行处理的，如果需要将文件里面多行处理为一行数据，可以通过设置multiLine=true（默认为false）
spark.read.option("multiLine","true").json("xxxxxxxx/xxxx.zip")
3、当zip或者gzip的文件没有任何后缀名或者后缀名不对时，那spark就无法自动读取了，但是此时可以通过类似如下的方式来读取
spark.read.format("binaryFile").load("dbfs:/xxx/data/xxxx/xxxx/2021/07/01/*")
读取到后，自己在代码中来解析处理读取的二进制文件数据
spark.read.format("binaryFile").load("dbfs:/xxx/data/xxxx/xxxx/2021/07/01/*").foreach(data=>{
// data解析
})
而且在读取到binaryFile文件后，还可以通过注册udf函数来进行处理 转载请注明：张永清 博客园：https://www.cnblogs.com/laoqing/p/15516458.html
spark在读取数据转换为dataframe时，是通过DataFrameReader.scala来处理的（https://github.com/apache/spark/blob/v3.1.2/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala）。从中可以看到option选项除了支持multiLine外，还支持了很多，从源码注释中可以看到，如下所示。
sep:default `,`
encoding:default `UTF-8` decodes the CSV files by the given encoding type
quote:default `"` sets a single character used for escaping quoted values where the separator can be part of the value. If you would like to turn off quotations, you need to set not `null` but an empty string. This behaviour is different from com.databricks.spark.csv
escape:default `\` sets a single character used for escaping quotes inside an already quoted value.
charToEscapeQuoteEscaping:default `escape` or `\0`
comment:default empty string
header:default `false`
enforceSchema:default `true`
inferSchema:(default `false`)
samplingRatio:default is 1.0
ignoreLeadingWhiteSpace:default `false`
ignoreTrailingWhiteSpace:default `false`
nullValue:default empty string
emptyValue:default empty string
nanValue:default `NaN`
positiveInf:default `Inf`
negativeInf:default `-Inf`
dateFormat:default `yyyy-MM-dd`
timestampFormat:default `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`
maxColumns:default `20480`
maxCharsPerColumn:default `-1`
unescapedQuoteHandling:default `STOP_AT_DELIMITER`
mode:default `PERMISSIVE`
columnNameOfCorruptRecord:default is the value specified in `spark.sql.columnNameOfCorruptRecord`
multiLine:default `false`
locale:default is `en-US`
lineSep:default covers all `\r`, `\r\n` and `\n`
pathGlobFilter:an optional glob pattern to only include files with paths matching the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>. It does not change the behavior of partition discovery.
modifiedBefore(batch only): an optional timestamp to only include files with modification times occurring before the specified Time. The provided timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
modifiedAfter(batch only):an optional timestamp to only include files with modification times occurring after the specified Time. The provided timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
recursiveFileLookup: recursively scan a directory for files. Using this option disables partition discovery
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spark/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala
@HyukjinKwon
HyukjinKwon [SPARK-35250][SQL][DOCS] Fix duplicated STOP_AT_DELIMITER to SKIP_VAL…
…
Latest commit 89f5ec7 on May 4
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@HyukjinKwon@cloud-fan@MaxGekk@rxin@srowen@liancheng@maropu@gatorsmile@viirya@gengliangwang@dongjoon-hyun@yaooqinn
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/*
* 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.spark.sql
import java.util.{Locale, Properties}
import scala.collection.JavaConverters._
import com.fasterxml.jackson.databind.ObjectMapper
import org.apache.spark.Partition
import org.apache.spark.annotation.Stable
import org.apache.spark.api.java.JavaRDD
import org.apache.spark.internal.Logging
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.catalyst.analysis.UnresolvedRelation
import org.apache.spark.sql.catalyst.csv.{CSVHeaderChecker, CSVOptions, UnivocityParser}
import org.apache.spark.sql.catalyst.expressions.ExprUtils
import org.apache.spark.sql.catalyst.json.{CreateJacksonParser, JacksonParser, JSONOptions}
import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, CharVarcharUtils, FailureSafeParser}
import org.apache.spark.sql.connector.catalog.{CatalogV2Util, SupportsCatalogOptions, SupportsRead}
import org.apache.spark.sql.connector.catalog.TableCapability._
import org.apache.spark.sql.execution.command.DDLUtils
import org.apache.spark.sql.execution.datasources.DataSource
import org.apache.spark.sql.execution.datasources.csv._
import org.apache.spark.sql.execution.datasources.jdbc._
import org.apache.spark.sql.execution.datasources.json.TextInputJsonDataSource
import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, DataSourceV2Utils}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.util.CaseInsensitiveStringMap
import org.apache.spark.unsafe.types.UTF8String
/**
* Interface used to load a [[Dataset]] from external storage systems (e.g. file systems,
* key-value stores, etc). Use `SparkSession.read` to access this.
*
* @since 1.4.0
*/
@Stable
class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
/**
* Specifies the input data source format.
*
* @since 1.4.0
*/
def format(source: String): DataFrameReader = {
this.source = source
this
}
/**
* Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema
* automatically from data. By specifying the schema here, the underlying data source can
* skip the schema inference step, and thus speed up data loading.
*
* @since 1.4.0
*/
def schema(schema: StructType): DataFrameReader = {
if (schema != null) {
val replaced = CharVarcharUtils.failIfHasCharVarchar(schema).asInstanceOf[StructType]
this.userSpecifiedSchema = Option(replaced)
}
this
}
/**
* Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can
* infer the input schema automatically from data. By specifying the schema here, the underlying
* data source can skip the schema inference step, and thus speed up data loading.
*
* {{{
* spark.read.schema("a INT, b STRING, c DOUBLE").csv("test.csv")
* }}}
*
* @since 2.3.0
*/
def schema(schemaString: String): DataFrameReader = {
schema(StructType.fromDDL(schemaString))
}
/**
* Adds an input option for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* You can set the following option(s):
* <ul>
* <li>`timeZone` (default session local timezone): sets the string that indicates a time zone ID
* to be used to parse timestamps in the JSON/CSV datasources or partition values. The following
* formats of `timeZone` are supported:
* <ul>
* <li> Region-based zone ID: It should have the form 'area/city', such as
* 'America/Los_Angeles'.</li>
* <li> Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00'
* or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.</li>
* </ul>
* Other short names like 'CST' are not recommended to use because they can be ambiguous.
* If it isn't set, the current value of the SQL config `spark.sql.session.timeZone` is
* used by default.
* </li>
* </ul>
*
* @since 1.4.0
*/
def option(key: String, value: String): DataFrameReader = {
this.extraOptions = this.extraOptions + (key -> value)
this
}
/**
* Adds an input option for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* @since 2.0.0
*/
def option(key: String, value: Boolean): DataFrameReader = option(key, value.toString)
/**
* Adds an input option for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* @since 2.0.0
*/
def option(key: String, value: Long): DataFrameReader = option(key, value.toString)
/**
* Adds an input option for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* @since 2.0.0
*/
def option(key: String, value: Double): DataFrameReader = option(key, value.toString)
/**
* (Scala-specific) Adds input options for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* You can set the following option(s):
* <ul>
* <li>`timeZone` (default session local timezone): sets the string that indicates a time zone ID
* to be used to parse timestamps in the JSON/CSV datasources or partition values. The following
* formats of `timeZone` are supported:
* <ul>
* <li> Region-based zone ID: It should have the form 'area/city', such as
* 'America/Los_Angeles'.</li>
* <li> Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00'
* or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.</li>
* </ul>
* Other short names like 'CST' are not recommended to use because they can be ambiguous.
* If it isn't set, the current value of the SQL config `spark.sql.session.timeZone` is
* used by default.
* </li>
* </ul>
*
* @since 1.4.0
*/
def options(options: scala.collection.Map[String, String]): DataFrameReader = {
this.extraOptions ++= options
this
}
/**
* Adds input options for the underlying data source.
*
* All options are maintained in a case-insensitive way in terms of key names.
* If a new option has the same key case-insensitively, it will override the existing option.
*
* You can set the following option(s):
* <ul>
* <li>`timeZone` (default session local timezone): sets the string that indicates a time zone ID
* to be used to parse timestamps in the JSON/CSV datasources or partition values. The following
* formats of `timeZone` are supported:
* <ul>
* <li> Region-based zone ID: It should have the form 'area/city', such as
* 'America/Los_Angeles'.</li>
* <li> Zone offset: It should be in the format '(+|-)HH:mm', for example '-08:00'
* or '+01:00'. Also 'UTC' and 'Z' are supported as aliases of '+00:00'.</li>
* </ul>
* Other short names like 'CST' are not recommended to use because they can be ambiguous.
* If it isn't set, the current value of the SQL config `spark.sql.session.timeZone` is
* used by default.
* </li>
* </ul>
*
* @since 1.4.0
*/
def options(options: java.util.Map[String, String]): DataFrameReader = {
this.options(options.asScala)
this
}
/**
* Loads input in as a `DataFrame`, for data sources that don't require a path (e.g. external
* key-value stores).
*
* @since 1.4.0
*/
def load(): DataFrame = {
load(Seq.empty: _*) // force invocation of `load(...varargs...)`
}
/**
* Loads input in as a `DataFrame`, for data sources that require a path (e.g. data backed by
* a local or distributed file system).
*
* @since 1.4.0
*/
def load(path: String): DataFrame = {
// force invocation of `load(...varargs...)`
if (sparkSession.sessionState.conf.legacyPathOptionBehavior) {
option("path", path).load(Seq.empty: _*)
} else {
load(Seq(path): _*)
}
}
/**
* Loads input in as a `DataFrame`, for data sources that support multiple paths.
* Only works if the source is a HadoopFsRelationProvider.
*
* @since 1.6.0
*/
@scala.annotation.varargs
def load(paths: String*): DataFrame = {
if (source.toLowerCase(Locale.ROOT) == DDLUtils.HIVE_PROVIDER) {
throw new AnalysisException("Hive data source can only be used with tables, you can not " +
"read files of Hive data source directly.")
}
val legacyPathOptionBehavior = sparkSession.sessionState.conf.legacyPathOptionBehavior
if (!legacyPathOptionBehavior &&
(extraOptions.contains("path") || extraOptions.contains("paths")) && paths.nonEmpty) {
throw new AnalysisException("There is a 'path' or 'paths' option set and load() is called " +
"with path parameters. Either remove the path option if it's the same as the path " +
"parameter, or add it to the load() parameter if you do want to read multiple paths. " +
s"To ignore this check, set '${SQLConf.LEGACY_PATH_OPTION_BEHAVIOR.key}' to 'true'.")
}
DataSource.lookupDataSourceV2(source, sparkSession.sessionState.conf).map { provider =>
val catalogManager = sparkSession.sessionState.catalogManager
val sessionOptions = DataSourceV2Utils.extractSessionConfigs(
source = provider, conf = sparkSession.sessionState.conf)
val optionsWithPath = if (paths.isEmpty) {
extraOptions
} else if (paths.length == 1) {
extraOptions + ("path" -> paths.head)
} else {
val objectMapper = new ObjectMapper()
extraOptions + ("paths" -> objectMapper.writeValueAsString(paths.toArray))
}
val finalOptions = sessionOptions.filterKeys(!optionsWithPath.contains(_)).toMap ++
optionsWithPath.originalMap
val dsOptions = new CaseInsensitiveStringMap(finalOptions.asJava)
val (table, catalog, ident) = provider match {
case _: SupportsCatalogOptions if userSpecifiedSchema.nonEmpty =>
throw new IllegalArgumentException(
s"$source does not support user specified schema. Please don't specify the schema.")
case hasCatalog: SupportsCatalogOptions =>
val ident = hasCatalog.extractIdentifier(dsOptions)
val catalog = CatalogV2Util.getTableProviderCatalog(
hasCatalog,
catalogManager,
dsOptions)
(catalog.loadTable(ident), Some(catalog), Some(ident))
case _ =>
// TODO: Non-catalog paths for DSV2 are currently not well defined.
val tbl = DataSourceV2Utils.getTableFromProvider(provider, dsOptions, userSpecifiedSchema)
(tbl, None, None)
}
import org.apache.spark.sql.execution.datasources.v2.DataSourceV2Implicits._
table match {
case _: SupportsRead if table.supports(BATCH_READ) =>
Dataset.ofRows(
sparkSession,
DataSourceV2Relation.create(table, catalog, ident, dsOptions))
case _ => loadV1Source(paths: _*)
}
}.getOrElse(loadV1Source(paths: _*))
}
private def loadV1Source(paths: String*) = {
val legacyPathOptionBehavior = sparkSession.sessionState.conf.legacyPathOptionBehavior
val (finalPaths, finalOptions) = if (!legacyPathOptionBehavior && paths.length == 1) {
(Nil, extraOptions + ("path" -> paths.head))
} else {
(paths, extraOptions)
}
// Code path for data source v1.
sparkSession.baseRelationToDataFrame(
DataSource.apply(
sparkSession,
paths = finalPaths,
userSpecifiedSchema = userSpecifiedSchema,
className = source,
options = finalOptions.originalMap).resolveRelation())
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table and connection properties.
*
* @since 1.4.0
*/
def jdbc(url: String, table: String, properties: Properties): DataFrame = {
assertNoSpecifiedSchema("jdbc")
// properties should override settings in extraOptions.
this.extraOptions ++= properties.asScala
// explicit url and dbtable should override all
this.extraOptions ++= Seq(JDBCOptions.JDBC_URL -> url, JDBCOptions.JDBC_TABLE_NAME -> table)
format("jdbc").load()
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table. Partitions of the table will be retrieved in parallel based on the parameters
* passed to this function.
*
* Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash
* your external database systems.
*
* @param url JDBC database url of the form `jdbc:subprotocol:subname`.
* @param table Name of the table in the external database.
* @param columnName the name of a column of numeric, date, or timestamp type
* that will be used for partitioning.
* @param lowerBound the minimum value of `columnName` used to decide partition stride.
* @param upperBound the maximum value of `columnName` used to decide partition stride.
* @param numPartitions the number of partitions. This, along with `lowerBound` (inclusive),
* `upperBound` (exclusive), form partition strides for generated WHERE
* clause expressions used to split the column `columnName` evenly. When
* the input is less than 1, the number is set to 1.
* @param connectionProperties JDBC database connection arguments, a list of arbitrary string
* tag/value. Normally at least a "user" and "password" property
* should be included. "fetchsize" can be used to control the
* number of rows per fetch and "queryTimeout" can be used to wait
* for a Statement object to execute to the given number of seconds.
* @since 1.4.0
*/
def jdbc(
url: String,
table: String,
columnName: String,
lowerBound: Long,
upperBound: Long,
numPartitions: Int,
connectionProperties: Properties): DataFrame = {
// columnName, lowerBound, upperBound and numPartitions override settings in extraOptions.
this.extraOptions ++= Map(
JDBCOptions.JDBC_PARTITION_COLUMN -> columnName,
JDBCOptions.JDBC_LOWER_BOUND -> lowerBound.toString,
JDBCOptions.JDBC_UPPER_BOUND -> upperBound.toString,
JDBCOptions.JDBC_NUM_PARTITIONS -> numPartitions.toString)
jdbc(url, table, connectionProperties)
}
/**
* Construct a `DataFrame` representing the database table accessible via JDBC URL
* url named table using connection properties. The `predicates` parameter gives a list
* expressions suitable for inclusion in WHERE clauses; each one defines one partition
* of the `DataFrame`.
*
* Don't create too many partitions in parallel on a large cluster; otherwise Spark might crash
* your external database systems.
*
* @param url JDBC database url of the form `jdbc:subprotocol:subname`
* @param table Name of the table in the external database.
* @param predicates Condition in the where clause for each partition.
* @param connectionProperties JDBC database connection arguments, a list of arbitrary string
* tag/value. Normally at least a "user" and "password" property
* should be included. "fetchsize" can be used to control the
* number of rows per fetch.
* @since 1.4.0
*/
def jdbc(
url: String,
table: String,
predicates: Array[String],
connectionProperties: Properties): DataFrame = {
assertNoSpecifiedSchema("jdbc")
// connectionProperties should override settings in extraOptions.
val params = extraOptions ++ connectionProperties.asScala
val options = new JDBCOptions(url, table, params)
val parts: Array[Partition] = predicates.zipWithIndex.map { case (part, i) =>
JDBCPartition(part, i) : Partition
}
val relation = JDBCRelation(parts, options)(sparkSession)
sparkSession.baseRelationToDataFrame(relation)
}
/**
* Loads a JSON file and returns the results as a `DataFrame`.
*
* See the documentation on the overloaded `json()` method with varargs for more details.
*
* @since 1.4.0
*/
def json(path: String): DataFrame = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
json(Seq(path): _*)
}
/**
* Loads JSON files and returns the results as a `DataFrame`.
*
* <a href="http://jsonlines.org/">JSON Lines</a> (newline-delimited JSON) is supported by
* default. For JSON (one record per file), set the `multiLine` option to true.
*
* This function goes through the input once to determine the input schema. If you know the
* schema in advance, use the version that specifies the schema to avoid the extra scan.
*
* You can set the following JSON-specific options to deal with non-standard JSON files:
* <ul>
* <li>`primitivesAsString` (default `false`): infers all primitive values as a string type</li>
* <li>`prefersDecimal` (default `false`): infers all floating-point values as a decimal
* type. If the values do not fit in decimal, then it infers them as doubles.</li>
* <li>`allowComments` (default `false`): ignores Java/C++ style comment in JSON records</li>
* <li>`allowUnquotedFieldNames` (default `false`): allows unquoted JSON field names</li>
* <li>`allowSingleQuotes` (default `true`): allows single quotes in addition to double quotes
* </li>
* <li>`allowNumericLeadingZeros` (default `false`): allows leading zeros in numbers
* (e.g. 00012)</li>
* <li>`allowBackslashEscapingAnyCharacter` (default `false`): allows accepting quoting of all
* character using backslash quoting mechanism</li>
* <li>`allowUnquotedControlChars` (default `false`): allows JSON Strings to contain unquoted
* control characters (ASCII characters with value less than 32, including tab and line feed
* characters) or not.</li>
* <li>`mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records
* during parsing.
* <ul>
* <li>`PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a
* field configured by `columnNameOfCorruptRecord`, and sets malformed fields to `null`. To
* keep corrupt records, an user can set a string type field named
* `columnNameOfCorruptRecord` in an user-defined schema. If a schema does not have the
* field, it drops corrupt records during parsing. When inferring a schema, it implicitly
* adds a `columnNameOfCorruptRecord` field in an output schema.</li>
* <li>`DROPMALFORMED` : ignores the whole corrupted records.</li>
* <li>`FAILFAST` : throws an exception when it meets corrupted records.</li>
* </ul>
* </li>
* <li>`columnNameOfCorruptRecord` (default is the value specified in
* `spark.sql.columnNameOfCorruptRecord`): allows renaming the new field having malformed string
* created by `PERMISSIVE` mode. This overrides `spark.sql.columnNameOfCorruptRecord`.</li>
* <li>`dateFormat` (default `yyyy-MM-dd`): sets the string that indicates a date format.
* Custom date formats follow the formats at
* <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">
* Datetime Patterns</a>.
* This applies to date type.</li>
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">
* Datetime Patterns</a>.
* This applies to timestamp type.</li>
* <li>`multiLine` (default `false`): parse one record, which may span multiple lines,
* per file</li>
* <li>`encoding` (by default it is not set): allows to forcibly set one of standard basic
* or extended encoding for the JSON files. For example UTF-16BE, UTF-32LE. If the encoding
* is not specified and `multiLine` is set to `true`, it will be detected automatically.</li>
* <li>`lineSep` (default covers all `\r`, `\r\n` and `\n`): defines the line separator
* that should be used for parsing.</li>
* <li>`samplingRatio` (default is 1.0): defines fraction of input JSON objects used
* for schema inferring.</li>
* <li>`dropFieldIfAllNull` (default `false`): whether to ignore column of all null values or
* empty array/struct during schema inference.</li>
* <li>`locale` (default is `en-US`): sets a locale as language tag in IETF BCP 47 format.
* For instance, this is used while parsing dates and timestamps.</li>
* <li>`pathGlobFilter`: an optional glob pattern to only include files with paths matching
* the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>.
* It does not change the behavior of partition discovery.</li>
* <li>`modifiedBefore` (batch only): an optional timestamp to only include files with
* modification times occurring before the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`modifiedAfter` (batch only): an optional timestamp to only include files with
* modification times occurring after the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`recursiveFileLookup`: recursively scan a directory for files. Using this option
* disables partition discovery</li>
* <li>`allowNonNumericNumbers` (default `true`): allows JSON parser to recognize set of
* "Not-a-Number" (NaN) tokens as legal floating number values:
* <ul>
* <li>`+INF` for positive infinity, as well as alias of `+Infinity` and `Infinity`.
* <li>`-INF` for negative infinity), alias `-Infinity`.
* <li>`NaN` for other not-a-numbers, like result of division by zero.
* </ul>
* </li>
* </ul>
*
* @since 2.0.0
*/
@scala.annotation.varargs
def json(paths: String*): DataFrame = format("json").load(paths : _*)
/**
* Loads a `JavaRDD[String]` storing JSON objects (<a href="http://jsonlines.org/">JSON
* Lines text format or newline-delimited JSON</a>) and returns the result as
* a `DataFrame`.
*
* Unless the schema is specified using `schema` function, this function goes through the
* input once to determine the input schema.
*
* @param jsonRDD input RDD with one JSON object per record
* @since 1.4.0
*/
@deprecated("Use json(Dataset[String]) instead.", "2.2.0")
def json(jsonRDD: JavaRDD[String]): DataFrame = json(jsonRDD.rdd)
/**
* Loads an `RDD[String]` storing JSON objects (<a href="http://jsonlines.org/">JSON Lines
* text format or newline-delimited JSON</a>) and returns the result as a `DataFrame`.
*
* Unless the schema is specified using `schema` function, this function goes through the
* input once to determine the input schema.
*
* @param jsonRDD input RDD with one JSON object per record
* @since 1.4.0
*/
@deprecated("Use json(Dataset[String]) instead.", "2.2.0")
def json(jsonRDD: RDD[String]): DataFrame = {
json(sparkSession.createDataset(jsonRDD)(Encoders.STRING))
}
/**
* Loads a `Dataset[String]` storing JSON objects (<a href="http://jsonlines.org/">JSON Lines
* text format or newline-delimited JSON</a>) and returns the result as a `DataFrame`.
*
* Unless the schema is specified using `schema` function, this function goes through the
* input once to determine the input schema.
*
* @param jsonDataset input Dataset with one JSON object per record
* @since 2.2.0
*/
def json(jsonDataset: Dataset[String]): DataFrame = {
val parsedOptions = new JSONOptions(
extraOptions.toMap,
sparkSession.sessionState.conf.sessionLocalTimeZone,
sparkSession.sessionState.conf.columnNameOfCorruptRecord)
val schema = userSpecifiedSchema.getOrElse {
TextInputJsonDataSource.inferFromDataset(jsonDataset, parsedOptions)
}
ExprUtils.verifyColumnNameOfCorruptRecord(schema, parsedOptions.columnNameOfCorruptRecord)
val actualSchema =
StructType(schema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord))
val createParser = CreateJacksonParser.string _
val parsed = jsonDataset.rdd.mapPartitions { iter =>
val rawParser = new JacksonParser(actualSchema, parsedOptions, allowArrayAsStructs = true)
val parser = new FailureSafeParser[String](
input => rawParser.parse(input, createParser, UTF8String.fromString),
parsedOptions.parseMode,
schema,
parsedOptions.columnNameOfCorruptRecord)
iter.flatMap(parser.parse)
}
sparkSession.internalCreateDataFrame(parsed, schema, isStreaming = jsonDataset.isStreaming)
}
/**
* Loads a CSV file and returns the result as a `DataFrame`. See the documentation on the
* other overloaded `csv()` method for more details.
*
* @since 2.0.0
*/
def csv(path: String): DataFrame = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
csv(Seq(path): _*)
}
/**
* Loads an `Dataset[String]` storing CSV rows and returns the result as a `DataFrame`.
*
* If the schema is not specified using `schema` function and `inferSchema` option is enabled,
* this function goes through the input once to determine the input schema.
*
* If the schema is not specified using `schema` function and `inferSchema` option is disabled,
* it determines the columns as string types and it reads only the first line to determine the
* names and the number of fields.
*
* If the enforceSchema is set to `false`, only the CSV header in the first line is checked
* to conform specified or inferred schema.
*
* @note if `header` option is set to `true` when calling this API, all lines same with
* the header will be removed if exists.
*
* @param csvDataset input Dataset with one CSV row per record
* @since 2.2.0
*/
def csv(csvDataset: Dataset[String]): DataFrame = {
val parsedOptions: CSVOptions = new CSVOptions(
extraOptions.toMap,
sparkSession.sessionState.conf.csvColumnPruning,
sparkSession.sessionState.conf.sessionLocalTimeZone)
val filteredLines: Dataset[String] =
CSVUtils.filterCommentAndEmpty(csvDataset, parsedOptions)
// For performance, short-circuit the collection of the first line when it won't be used:
// - TextInputCSVDataSource - Only uses firstLine to infer an unspecified schema
// - CSVHeaderChecker - Only uses firstLine to check header, when headerFlag is true
// - CSVUtils - Only uses firstLine to filter headers, when headerFlag is true
// (If the downstream logic grows more complicated, consider refactoring to an approach that
// delegates this decision to the constituent consumers themselves.)
val maybeFirstLine: Option[String] =
if (userSpecifiedSchema.isEmpty || parsedOptions.headerFlag) {
filteredLines.take(1).headOption
} else {
None
}
val schema = userSpecifiedSchema.getOrElse {
TextInputCSVDataSource.inferFromDataset(
sparkSession,
csvDataset,
maybeFirstLine,
parsedOptions)
}
ExprUtils.verifyColumnNameOfCorruptRecord(schema, parsedOptions.columnNameOfCorruptRecord)
val actualSchema =
StructType(schema.filterNot(_.name == parsedOptions.columnNameOfCorruptRecord))
val linesWithoutHeader: RDD[String] = maybeFirstLine.map { firstLine =>
val headerChecker = new CSVHeaderChecker(
actualSchema,
parsedOptions,
source = s"CSV source: $csvDataset")
headerChecker.checkHeaderColumnNames(firstLine)
filteredLines.rdd.mapPartitions(CSVUtils.filterHeaderLine(_, firstLine, parsedOptions))
}.getOrElse(filteredLines.rdd)
val parsed = linesWithoutHeader.mapPartitions { iter =>
val rawParser = new UnivocityParser(actualSchema, parsedOptions)
val parser = new FailureSafeParser[String](
input => rawParser.parse(input),
parsedOptions.parseMode,
schema,
parsedOptions.columnNameOfCorruptRecord)
iter.flatMap(parser.parse)
}
sparkSession.internalCreateDataFrame(parsed, schema, isStreaming = csvDataset.isStreaming)
}
/**
* Loads CSV files and returns the result as a `DataFrame`.
*
* This function will go through the input once to determine the input schema if `inferSchema`
* is enabled. To avoid going through the entire data once, disable `inferSchema` option or
* specify the schema explicitly using `schema`.
*
* You can set the following CSV-specific options to deal with CSV files:
* <ul>
* <li>`sep` (default `,`): sets a separator for each field and value. This separator can be one
* or more characters.</li>
* <li>`encoding` (default `UTF-8`): decodes the CSV files by the given encoding
* type.</li>
* <li>`quote` (default `"`): sets a single character used for escaping quoted values where
* the separator can be part of the value. If you would like to turn off quotations, you need to
* set not `null` but an empty string. This behaviour is different from
* `com.databricks.spark.csv`.</li>
* <li>`escape` (default `\`): sets a single character used for escaping quotes inside
* an already quoted value.</li>
* <li>`charToEscapeQuoteEscaping` (default `escape` or `\0`): sets a single character used for
* escaping the escape for the quote character. The default value is escape character when escape
* and quote characters are different, `\0` otherwise.</li>
* <li>`comment` (default empty string): sets a single character used for skipping lines
* beginning with this character. By default, it is disabled.</li>
* <li>`header` (default `false`): uses the first line as names of columns.</li>
* <li>`enforceSchema` (default `true`): If it is set to `true`, the specified or inferred schema
* will be forcibly applied to datasource files, and headers in CSV files will be ignored.
* If the option is set to `false`, the schema will be validated against all headers in CSV files
* in the case when the `header` option is set to `true`. Field names in the schema
* and column names in CSV headers are checked by their positions taking into account
* `spark.sql.caseSensitive`. Though the default value is true, it is recommended to disable
* the `enforceSchema` option to avoid incorrect results.</li>
* <li>`inferSchema` (default `false`): infers the input schema automatically from data. It
* requires one extra pass over the data.</li>
* <li>`samplingRatio` (default is 1.0): defines fraction of rows used for schema inferring.</li>
* <li>`ignoreLeadingWhiteSpace` (default `false`): a flag indicating whether or not leading
* whitespaces from values being read should be skipped.</li>
* <li>`ignoreTrailingWhiteSpace` (default `false`): a flag indicating whether or not trailing
* whitespaces from values being read should be skipped.</li>
* <li>`nullValue` (default empty string): sets the string representation of a null value. Since
* 2.0.1, this applies to all supported types including the string type.</li>
* <li>`emptyValue` (default empty string): sets the string representation of an empty value.</li>
* <li>`nanValue` (default `NaN`): sets the string representation of a non-number" value.</li>
* <li>`positiveInf` (default `Inf`): sets the string representation of a positive infinity
* value.</li>
* <li>`negativeInf` (default `-Inf`): sets the string representation of a negative infinity
* value.</li>
* <li>`dateFormat` (default `yyyy-MM-dd`): sets the string that indicates a date format.
* Custom date formats follow the formats at
* <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">
* Datetime Patterns</a>.
* This applies to date type.</li>
* <li>`timestampFormat` (default `yyyy-MM-dd'T'HH:mm:ss[.SSS][XXX]`): sets the string that
* indicates a timestamp format. Custom date formats follow the formats at
* <a href="https://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html">
* Datetime Patterns</a>.
* This applies to timestamp type.</li>
* <li>`maxColumns` (default `20480`): defines a hard limit of how many columns
* a record can have.</li>
* <li>`maxCharsPerColumn` (default `-1`): defines the maximum number of characters allowed
* for any given value being read. By default, it is -1 meaning unlimited length</li>
* <li>`unescapedQuoteHandling` (default `STOP_AT_DELIMITER`): defines how the CsvParser
* will handle values with unescaped quotes.
* <ul>
* <li>`STOP_AT_CLOSING_QUOTE`: If unescaped quotes are found in the input, accumulate
* the quote character and proceed parsing the value as a quoted value, until a closing
* quote is found.</li>
* <li>`BACK_TO_DELIMITER`: If unescaped quotes are found in the input, consider the value
* as an unquoted value. This will make the parser accumulate all characters of the current
* parsed value until the delimiter is found. If no
* delimiter is found in the value, the parser will continue accumulating characters from
* the input until a delimiter or line ending is found.</li>
* <li>`STOP_AT_DELIMITER`: If unescaped quotes are found in the input, consider the value
* as an unquoted value. This will make the parser accumulate all characters until the
* delimiter or a line ending is found in the input.</li>
* <li>`SKIP_VALUE`: If unescaped quotes are found in the input, the content parsed
* for the given value will be skipped and the value set in nullValue will be produced
* instead.</li>
* <li>`RAISE_ERROR`: If unescaped quotes are found in the input, a TextParsingException
* will be thrown.</li>
* </ul>
* </li>
* <li>`mode` (default `PERMISSIVE`): allows a mode for dealing with corrupt records
* during parsing. It supports the following case-insensitive modes. Note that Spark tries
* to parse only required columns in CSV under column pruning. Therefore, corrupt records
* can be different based on required set of fields. This behavior can be controlled by
* `spark.sql.csv.parser.columnPruning.enabled` (enabled by default).
* <ul>
* <li>`PERMISSIVE` : when it meets a corrupted record, puts the malformed string into a
* field configured by `columnNameOfCorruptRecord`, and sets malformed fields to `null`.
* To keep corrupt records, an user can set a string type field named
* `columnNameOfCorruptRecord` in an user-defined schema. If a schema does not have
* the field, it drops corrupt records during parsing. A record with less/more tokens
* than schema is not a corrupted record to CSV. When it meets a record having fewer
* tokens than the length of the schema, sets `null` to extra fields. When the record
* has more tokens than the length of the schema, it drops extra tokens.</li>
* <li>`DROPMALFORMED` : ignores the whole corrupted records.</li>
* <li>`FAILFAST` : throws an exception when it meets corrupted records.</li>
* </ul>
* </li>
* <li>`columnNameOfCorruptRecord` (default is the value specified in
* `spark.sql.columnNameOfCorruptRecord`): allows renaming the new field having malformed string
* created by `PERMISSIVE` mode. This overrides `spark.sql.columnNameOfCorruptRecord`.</li>
* <li>`multiLine` (default `false`): parse one record, which may span multiple lines.</li>
* <li>`locale` (default is `en-US`): sets a locale as language tag in IETF BCP 47 format.
* For instance, this is used while parsing dates and timestamps.</li>
* <li>`lineSep` (default covers all `\r`, `\r\n` and `\n`): defines the line separator
* that should be used for parsing. Maximum length is 1 character.</li>
* <li>`pathGlobFilter`: an optional glob pattern to only include files with paths matching
* the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>.
* It does not change the behavior of partition discovery.</li>
* <li>`modifiedBefore` (batch only): an optional timestamp to only include files with
* modification times occurring before the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`modifiedAfter` (batch only): an optional timestamp to only include files with
* modification times occurring after the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`recursiveFileLookup`: recursively scan a directory for files. Using this option
* disables partition discovery</li>
* </ul>
*
* @since 2.0.0
*/
@scala.annotation.varargs
def csv(paths: String*): DataFrame = format("csv").load(paths : _*)
/**
* Loads a Parquet file, returning the result as a `DataFrame`. See the documentation
* on the other overloaded `parquet()` method for more details.
*
* @since 2.0.0
*/
def parquet(path: String): DataFrame = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
parquet(Seq(path): _*)
}
/**
* Loads a Parquet file, returning the result as a `DataFrame`.
*
* You can set the following Parquet-specific option(s) for reading Parquet files:
* <ul>
* <li>`mergeSchema` (default is the value specified in `spark.sql.parquet.mergeSchema`): sets
* whether we should merge schemas collected from all Parquet part-files. This will override
* `spark.sql.parquet.mergeSchema`.</li>
* <li>`pathGlobFilter`: an optional glob pattern to only include files with paths matching
* the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>.
* It does not change the behavior of partition discovery.</li>
* <li>`modifiedBefore` (batch only): an optional timestamp to only include files with
* modification times occurring before the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`modifiedAfter` (batch only): an optional timestamp to only include files with
* modification times occurring after the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`recursiveFileLookup`: recursively scan a directory for files. Using this option
* disables partition discovery</li>
* </ul>
*
* @since 1.4.0
*/
@scala.annotation.varargs
def parquet(paths: String*): DataFrame = {
format("parquet").load(paths: _*)
}
/**
* Loads an ORC file and returns the result as a `DataFrame`.
*
* @param path input path
* @since 1.5.0
*/
def orc(path: String): DataFrame = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
orc(Seq(path): _*)
}
/**
* Loads ORC files and returns the result as a `DataFrame`.
*
* You can set the following ORC-specific option(s) for reading ORC files:
* <ul>
* <li>`mergeSchema` (default is the value specified in `spark.sql.orc.mergeSchema`): sets whether
* we should merge schemas collected from all ORC part-files. This will override
* `spark.sql.orc.mergeSchema`.</li>
* <li>`pathGlobFilter`: an optional glob pattern to only include files with paths matching
* the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>.
* It does not change the behavior of partition discovery.</li>
* <li>`modifiedBefore` (batch only): an optional timestamp to only include files with
* modification times occurring before the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`modifiedAfter` (batch only): an optional timestamp to only include files with
* modification times occurring after the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`recursiveFileLookup`: recursively scan a directory for files. Using this option
* disables partition discovery</li>
* </ul>
*
* @param paths input paths
* @since 2.0.0
*/
@scala.annotation.varargs
def orc(paths: String*): DataFrame = format("orc").load(paths: _*)
/**
* Returns the specified table/view as a `DataFrame`. If it's a table, it must support batch
* reading and the returned DataFrame is the batch scan query plan of this table. If it's a view,
* the returned DataFrame is simply the query plan of the view, which can either be a batch or
* streaming query plan.
*
* @param tableName is either a qualified or unqualified name that designates a table or view.
* If a database is specified, it identifies the table/view from the database.
* Otherwise, it first attempts to find a temporary view with the given name
* and then match the table/view from the current database.
* Note that, the global temporary view database is also valid here.
* @since 1.4.0
*/
def table(tableName: String): DataFrame = {
assertNoSpecifiedSchema("table")
val multipartIdentifier =
sparkSession.sessionState.sqlParser.parseMultipartIdentifier(tableName)
Dataset.ofRows(sparkSession, UnresolvedRelation(multipartIdentifier,
new CaseInsensitiveStringMap(extraOptions.toMap.asJava)))
}
/**
* Loads text files and returns a `DataFrame` whose schema starts with a string column named
* "value", and followed by partitioned columns if there are any. See the documentation on
* the other overloaded `text()` method for more details.
*
* @since 2.0.0
*/
def text(path: String): DataFrame = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
text(Seq(path): _*)
}
/**
* Loads text files and returns a `DataFrame` whose schema starts with a string column named
* "value", and followed by partitioned columns if there are any.
* The text files must be encoded as UTF-8.
*
* By default, each line in the text files is a new row in the resulting DataFrame. For example:
* {{{
* // Scala:
* spark.read.text("/path/to/spark/README.md")
*
* // Java:
* spark.read().text("/path/to/spark/README.md")
* }}}
*
* You can set the following text-specific option(s) for reading text files:
* <ul>
* <li>`wholetext` (default `false`): If true, read a file as a single row and not split by "\n".
* </li>
* <li>`lineSep` (default covers all `\r`, `\r\n` and `\n`): defines the line separator
* that should be used for parsing.</li>
* <li>`pathGlobFilter`: an optional glob pattern to only include files with paths matching
* the pattern. The syntax follows <code>org.apache.hadoop.fs.GlobFilter</code>.
* It does not change the behavior of partition discovery.</li>
* <li>`modifiedBefore` (batch only): an optional timestamp to only include files with
* modification times occurring before the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`modifiedAfter` (batch only): an optional timestamp to only include files with
* modification times occurring after the specified Time. The provided timestamp
* must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)</li>
* <li>`recursiveFileLookup`: recursively scan a directory for files. Using this option
* disables partition discovery</li>
* </ul>
*
* @param paths input paths
* @since 1.6.0
*/
@scala.annotation.varargs
def text(paths: String*): DataFrame = format("text").load(paths : _*)
/**
* Loads text files and returns a [[Dataset]] of String. See the documentation on the
* other overloaded `textFile()` method for more details.
* @since 2.0.0
*/
def textFile(path: String): Dataset[String] = {
// This method ensures that calls that explicit need single argument works, see SPARK-16009
textFile(Seq(path): _*)
}
/**
* Loads text files and returns a [[Dataset]] of String. The underlying schema of the Dataset
* contains a single string column named "value".
* The text files must be encoded as UTF-8.
*
* If the directory structure of the text files contains partitioning information, those are
* ignored in the resulting Dataset. To include partitioning information as columns, use `text`.
*
* By default, each line in the text files is a new row in the resulting DataFrame. For example:
* {{{
* // Scala:
* spark.read.textFile("/path/to/spark/README.md")
*
* // Java:
* spark.read().textFile("/path/to/spark/README.md")
* }}}
*
* You can set the text-specific options as specified in `DataFrameReader.text`.
*
* @param paths input path
* @since 2.0.0
*/
@scala.annotation.varargs
def textFile(paths: String*): Dataset[String] = {
assertNoSpecifiedSchema("textFile")
text(paths : _*).select("value").as[String](sparkSession.implicits.newStringEncoder)
}
/**
* A convenient function for schema validation in APIs.
*/
private def assertNoSpecifiedSchema(operation: String): Unit = {
if (userSpecifiedSchema.nonEmpty) {
throw new AnalysisException(s"User specified schema not supported with `$operation`")
}
}
///////////////////////////////////////////////////////////////////////////////////////
// Builder pattern config options
///////////////////////////////////////////////////////////////////////////////////////
private var source: String = sparkSession.sessionState.conf.defaultDataSourceName
private var userSpecifiedSchema: Option[StructType] = None
private var extraOptions = CaseInsensitiveMap[String](Map.empty)
}
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About
二、spark读取excel文件
引入如下依赖
Scala 2.12
groupId: com.crealytics artifactId: spark-excel_2.12 version: <spark-version>_0.14.0
Scala 2.11
groupId: com.crealytics artifactId: spark-excel_2.11 version: <spark-version>_0.14.0
Scala API
Spark 2.0+:
Create a DataFrame from an Excel file
import org.apache.spark.sql._
val spark: SparkSession = ???
val df = spark.read
.format("com.crealytics.spark.excel")
.option("dataAddress", "'My Sheet'!B3:C35") // Optional, default: "A1"
.option("header", "true") // Required
.option("treatEmptyValuesAsNulls", "false") // Optional, default: true
.option("setErrorCellsToFallbackValues", "true") // Optional, default: false, where errors will be converted to null. If true, any ERROR cell values (e.g. #N/A) will be converted to the zero values of the column's data type.
.option("usePlainNumberFormat", "false") // Optional, default: false, If true, format the cells without rounding and scientific notations
.option("inferSchema", "false") // Optional, default: false
.option("addColorColumns", "true") // Optional, default: false
.option("timestampFormat", "MM-dd-yyyy HH:mm:ss") // Optional, default: yyyy-mm-dd hh:mm:ss[.fffffffff]
.option("maxRowsInMemory", 20) // Optional, default None. If set, uses a streaming reader which can help with big files (will fail if used with xls format files)
.option("excerptSize", 10) // Optional, default: 10. If set and if schema inferred, number of rows to infer schema from
.option("workbookPassword", "pass") // Optional, default None. Requires unlimited strength JCE for older JVMs
.schema(myCustomSchema) // Optional, default: Either inferred schema, or all columns are Strings
.load("Worktime.xlsx")
For convenience, there is an implicit that wraps the DataFrameReader returned by spark.read and provides a .excel method which accepts all possible options and provides default values:
import org.apache.spark.sql._
import com.crealytics.spark.excel._
val spark: SparkSession = ???
val df = spark.read.excel(
header = true, // Required
dataAddress = "'My Sheet'!B3:C35", // Optional, default: "A1"
treatEmptyValuesAsNulls = false, // Optional, default: true
setErrorCellsToFallbackValues = false, // Optional, default: false, where errors will be converted to null. If true, any ERROR cell values (e.g. #N/A) will be converted to the zero values of the column's data type.
usePlainNumberFormat = false, // Optional, default: false. If true, format the cells without rounding and scientific notations
inferSchema = false, // Optional, default: false
addColorColumns = true, // Optional, default: false
timestampFormat = "MM-dd-yyyy HH:mm:ss", // Optional, default: yyyy-mm-dd hh:mm:ss[.fffffffff]
maxRowsInMemory = 20, // Optional, default None. If set, uses a streaming reader which can help with big files (will fail if used with xls format files)
excerptSize = 10, // Optional, default: 10. If set and if schema inferred, number of rows to infer schema from
workbookPassword = "pass" // Optional, default None. Requires unlimited strength JCE for older JVMs
).schema(myCustomSchema) // Optional, default: Either inferred schema, or all columns are Strings
.load("Worktime.xlsx")
If the sheet name is unavailable, it is possible to pass in an index:
val df = spark.read.excel(
header = true,
dataAddress = "0!B3:C35"
).load("Worktime.xlsx")
or to read in the names dynamically:
val sheetNames = WorkbookReader( Map("path" -> "Worktime.xlsx")
, spark.sparkContext.hadoopConfiguration
).sheetNames
val df = spark.read.excel(
header = true,
dataAddress = sheetNames(0)
)
Create a DataFrame from an Excel file using custom schema
import org.apache.spark.sql._
import org.apache.spark.sql.types._
val peopleSchema = StructType(Array(
StructField("Name", StringType, nullable = false),
StructField("Age", DoubleType, nullable = false),
StructField("Occupation", StringType, nullable = false),
StructField("Date of birth", StringType, nullable = false)))
val spark: SparkSession = ???
val df = spark.read
.format("com.crealytics.spark.excel")
.option("dataAddress", "'Info'!A1")
.option("header", "true")
.schema(peopleSchema)
.load("People.xlsx")
Write a DataFrame to an Excel file
import org.apache.spark.sql._
val df: DataFrame = ???
df.write
.format("com.crealytics.spark.excel")
.option("dataAddress", "'My Sheet'!B3:C35")
.option("header", "true")
.option("dateFormat", "yy-mmm-d") // Optional, default: yy-m-d h:mm
.option("timestampFormat", "mm-dd-yyyy hh:mm:ss") // Optional, default: yyyy-mm-dd hh:mm:ss.000
.mode("append") // Optional, default: overwrite.
.save("Worktime2.xlsx")
Data Addresses
As you can see in the examples above, the location of data to read or write can be specified with the dataAddress option. Currently the following address styles are supported:
B3: Start cell of the data. Reading will return all rows below and all columns to the right. Writing will start here and use as many columns and rows as required.B3:F35: Cell range of data. Reading will return only rows and columns in the specified range. Writing will start in the first cell (B3in this example) and use only the specified columns and rows. If there are more rows or columns in the DataFrame to write, they will be truncated. Make sure this is what you want.'My Sheet'!B3:F35: Same as above, but with a specific sheet.MyTable[#All]: Table of data. Reading will return all rows and columns in this table. Writing will only write within the current range of the table. No growing of the table will be performed. PRs to change this are welcome.
更多详细可以参考：
https://github.com/crealytics/spark-excel/