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第62课：SparkSQL下的Parquet使用最佳实践和代码实践学习笔记
本期内容：
1 SparkSQL下的Parquet使用最佳实践
2 SparkSQL下的Parquet实战
一：Spark SQL下的Parquet使用最佳实践
1， 过去整个业界对大数据的分析的技术栈的Pipeline一般分为以下两种方式：
a) Data Source->HDFS->MR/Hive/Spark(相当于ETL)->HDFS Parquet->Spark SQL/Impala->Result Service(可以放在DB中，也有可能被通过JDBC/ODBC来作为数据服务使用)；
b) Data Source->Real time update data to HBase/DB->Export to Parquet->Spark SQL/Impala-> Result Service(可以放在DB中，也有可能被通过JDBC/ODBC来作为数据服务使用)；
上述的第二种方式完全可以通过Kafka+Spark Streaming+Spark SQL（内部也强烈建议采用Parquet的方式来存储数据）的方式取代。
任何情况下都需要实时处理！人脸识别、信用卡诈骗等都是基于流处理。
2， 期待的方式：Data Source->Kafka->Spark Streaming->Parquet->Spark SQL(SparkSQL可以结合ML、GraphX等)->Parquet->其它各种Data Mining等
二：Parquet的精要介绍
1， Parquet是列式存储格式的一种文件类型，列式存储有以下的核心优势：
a.可以跳过不符合条件的数据，只读取需要的数据，降低IO数据量。
b.压缩编码可以降低磁盘存储空间。由于同一列的数据类型是一样的，可以使用更高效的压缩编码（例如Run Length Encoding和Delta Encoding）进一步节约存储空间。
c.只读取需要的列，支持向量运算，能够获取更好的扫描性能。
三．下面编写代码读取parquet文件内容并打印：
package SparkSQLByJava;
import java.util.List;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
public class SparkSQLParquetOps {
public static void main(String[] args) {
SparkConf conf = new SparkConf().setMaster("local").setAppName("SparkSQLParquetOps");
JavaSparkContext sc = new JavaSparkContext(conf);
SQLContext sqlContext = new SQLContext(sc);
DataFrame usersDF = sqlContext.read().parquet("D:\\DT-IMF\\testdata\\users.parquet");
//注册成为临时表以供后续的SQL查询操作
usersDF.registerTempTable("users");
//进行数据的多维度分析
DataFrame result = sqlContext.sql("select * from users");
//对结果进行处理，包括由DataFrame转换成为RDD<Row>,以及结构持久化。
List<Row> listRow = result.javaRDD().collect();
for(Row row : listRow){
System.out.println(row);
}
}
}
在eclipse中的运行console:
Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
16/04/02 09:17:56 INFO SparkContext: Running Spark version 1.6.0
16/04/02 09:18:07 INFO SecurityManager: Changing view acls to: think
16/04/02 09:18:07 INFO SecurityManager: Changing modify acls to: think
16/04/02 09:18:07 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(think); users with modify permissions: Set(think)
16/04/02 09:18:09 INFO Utils: Successfully started service 'sparkDriver' on port 60088.
16/04/02 09:18:11 INFO Slf4jLogger: Slf4jLogger started
16/04/02 09:18:11 INFO Remoting: Starting remoting
16/04/02 09:18:11 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkDriverActorSystem@192.168.56.1:60101]
16/04/02 09:18:11 INFO Utils: Successfully started service 'sparkDriverActorSystem' on port 60101.
16/04/02 09:18:11 INFO SparkEnv: Registering MapOutputTracker
16/04/02 09:18:12 INFO SparkEnv: Registering BlockManagerMaster
16/04/02 09:18:12 INFO DiskBlockManager: Created local directory at C:\Users\think\AppData\Local\Temp\blockmgr-c045274d-ef94-471d-819a-93e044022e60
16/04/02 09:18:12 INFO MemoryStore: MemoryStore started with capacity 1773.8 MB
16/04/02 09:18:12 INFO SparkEnv: Registering OutputCommitCoordinator
16/04/02 09:18:13 INFO Utils: Successfully started service 'SparkUI' on port 4040.
16/04/02 09:18:13 INFO SparkUI: Started SparkUI at http://192.168.56.1:4040
16/04/02 09:18:13 INFO Executor: Starting executor ID driver on host localhost
16/04/02 09:18:13 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 60108.
16/04/02 09:18:13 INFO NettyBlockTransferService: Server created on 60108
16/04/02 09:18:13 INFO BlockManagerMaster: Trying to register BlockManager
16/04/02 09:18:13 INFO BlockManagerMasterEndpoint: Registering block manager localhost:60108 with 1773.8 MB RAM, BlockManagerId(driver, localhost, 60108)
16/04/02 09:18:13 INFO BlockManagerMaster: Registered BlockManager
16/04/02 09:18:17 WARN : Your hostname, think-PC resolves to a loopback/non-reachable address: fe80:0:0:0:d401:a5b5:2103:6d13%eth8, but we couldn't find any external IP address!
16/04/02 09:18:18 INFO ParquetRelation: Listing file:/D:/DT-IMF/testdata/users.parquet on driver
16/04/02 09:18:20 INFO SparkContext: Starting job: parquet at SparkSQLParquetOps.java:16
16/04/02 09:18:20 INFO DAGScheduler: Got job 0 (parquet at SparkSQLParquetOps.java:16) with 1 output partitions
16/04/02 09:18:20 INFO DAGScheduler: Final stage: ResultStage 0 (parquet at SparkSQLParquetOps.java:16)
16/04/02 09:18:20 INFO DAGScheduler: Parents of final stage: List()
16/04/02 09:18:20 INFO DAGScheduler: Missing parents: List()
16/04/02 09:18:20 INFO DAGScheduler: Submitting ResultStage 0 (MapPartitionsRDD[1] at parquet at SparkSQLParquetOps.java:16), which has no missing parents
16/04/02 09:18:20 INFO MemoryStore: Block broadcast_0 stored as values in memory (estimated size 61.5 KB, free 61.5 KB)
16/04/02 09:18:20 INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory (estimated size 20.6 KB, free 82.1 KB)
16/04/02 09:18:20 INFO BlockManagerInfo: Added broadcast_0_piece0 in memory on localhost:60108 (size: 20.6 KB, free: 1773.7 MB)
16/04/02 09:18:20 INFO SparkContext: Created broadcast 0 from broadcast at DAGScheduler.scala:1006
16/04/02 09:18:20 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 0 (MapPartitionsRDD[1] at parquet at SparkSQLParquetOps.java:16)
16/04/02 09:18:20 INFO TaskSchedulerImpl: Adding task set 0.0 with 1 tasks
16/04/02 09:18:21 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, localhost, partition 0,PROCESS_LOCAL, 2180 bytes)
16/04/02 09:18:21 INFO Executor: Running task 0.0 in stage 0.0 (TID 0)
16/04/02 09:18:21 INFO ParquetFileReader: Initiating action with parallelism: 5
SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
16/04/02 09:18:24 INFO Executor: Finished task 0.0 in stage 0.0 (TID 0). 1842 bytes result sent to driver
16/04/02 09:18:24 INFO TaskSetManager: Finished task 0.0 in stage 0.0 (TID 0) in 3301 ms on localhost (1/1)
16/04/02 09:18:24 INFO TaskSchedulerImpl: Removed TaskSet 0.0, whose tasks have all completed, from pool
16/04/02 09:18:24 INFO DAGScheduler: ResultStage 0 (parquet at SparkSQLParquetOps.java:16) finished in 3.408 s
16/04/02 09:18:24 INFO DAGScheduler: Job 0 finished: parquet at SparkSQLParquetOps.java:16, took 4.121836 s
16/04/02 09:18:26 INFO MemoryStore: Block broadcast_1 stored as values in memory (estimated size 61.8 KB, free 143.9 KB)
16/04/02 09:18:26 INFO MemoryStore: Block broadcast_1_piece0 stored as bytes in memory (estimated size 19.3 KB, free 163.2 KB)
16/04/02 09:18:26 INFO BlockManagerInfo: Added broadcast_1_piece0 in memory on localhost:60108 (size: 19.3 KB, free: 1773.7 MB)
16/04/02 09:18:26 INFO SparkContext: Created broadcast 1 from javaRDD at SparkSQLParquetOps.java:23
16/04/02 09:18:28 INFO deprecation: mapred.min.split.size is deprecated. Instead, use mapreduce.input.fileinputformat.split.minsize
16/04/02 09:18:28 INFO ParquetRelation: Reading Parquet file(s) from file:/D:/DT-IMF/testdata/users.parquet
16/04/02 09:18:28 INFO SparkContext: Starting job: collect at SparkSQLParquetOps.java:23
16/04/02 09:18:28 INFO DAGScheduler: Got job 1 (collect at SparkSQLParquetOps.java:23) with 1 output partitions
16/04/02 09:18:28 INFO DAGScheduler: Final stage: ResultStage 1 (collect at SparkSQLParquetOps.java:23)
16/04/02 09:18:28 INFO DAGScheduler: Parents of final stage: List()
16/04/02 09:18:28 INFO DAGScheduler: Missing parents: List()
16/04/02 09:18:28 INFO DAGScheduler: Submitting ResultStage 1 (MapPartitionsRDD[3] at javaRDD at SparkSQLParquetOps.java:23), which has no missing parents
16/04/02 09:18:28 INFO MemoryStore: Block broadcast_2 stored as values in memory (estimated size 4.6 KB, free 167.8 KB)
16/04/02 09:18:28 INFO MemoryStore: Block broadcast_2_piece0 stored as bytes in memory (estimated size 2.6 KB, free 170.4 KB)
16/04/02 09:18:28 INFO BlockManagerInfo: Added broadcast_2_piece0 in memory on localhost:60108 (size: 2.6 KB, free: 1773.7 MB)
16/04/02 09:18:28 INFO SparkContext: Created broadcast 2 from broadcast at DAGScheduler.scala:1006
16/04/02 09:18:28 INFO DAGScheduler: Submitting 1 missing tasks from ResultStage 1 (MapPartitionsRDD[3] at javaRDD at SparkSQLParquetOps.java:23)
16/04/02 09:18:28 INFO TaskSchedulerImpl: Adding task set 1.0 with 1 tasks
16/04/02 09:18:28 INFO TaskSetManager: Starting task 0.0 in stage 1.0 (TID 1, localhost, partition 0,PROCESS_LOCAL, 2179 bytes)
16/04/02 09:18:28 INFO Executor: Running task 0.0 in stage 1.0 (TID 1)
16/04/02 09:18:28 INFO ParquetRelation$$anonfun$buildInternalScan$1$$anon$1: Input split: ParquetInputSplit{part: file:/D:/DT-IMF/testdata/users.parquet start: 0 end: 615 length: 615 hosts: []}
16/04/02 09:18:28 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
16/04/02 09:18:28 INFO CatalystReadSupport: Going to read the following fields from the Parquet file:
Parquet form:
message spark_schema {
required binary name (UTF8);
optional binary favorite_color (UTF8);
required group favorite_numbers (LIST) {
repeated int32 array;
}
}
Catalyst form:
StructType(StructField(name,StringType,false), StructField(favorite_color,StringType,true), StructField(favorite_numbers,ArrayType(IntegerType,false),false))
16/04/02 09:18:29 INFO BlockManagerInfo: Removed broadcast_0_piece0 on localhost:60108 in memory (size: 20.6 KB, free: 1773.7 MB)
16/04/02 09:18:29 INFO ContextCleaner: Cleaned accumulator 1
16/04/02 09:18:29 INFO GenerateUnsafeProjection: Code generated in 422.989887 ms
16/04/02 09:18:29 INFO InternalParquetRecordReader: RecordReader initialized will read a total of 2 records.
16/04/02 09:18:29 INFO InternalParquetRecordReader: at row 0. reading next block
16/04/02 09:18:29 INFO CodecPool: Got brand-new decompressor [.snappy]
16/04/02 09:18:29 INFO InternalParquetRecordReader: block read in memory in 54 ms. row count = 2
16/04/02 09:18:30 INFO Executor: Finished task 0.0 in stage 1.0 (TID 1). 3532 bytes result sent to driver
16/04/02 09:18:30 INFO TaskSetManager: Finished task 0.0 in stage 1.0 (TID 1) in 1796 ms on localhost (1/1)
16/04/02 09:18:30 INFO DAGScheduler: ResultStage 1 (collect at SparkSQLParquetOps.java:23) finished in 1.798 s
16/04/02 09:18:30 INFO TaskSchedulerImpl: Removed TaskSet 1.0, whose tasks have all completed, from pool
16/04/02 09:18:30 INFO DAGScheduler: Job 1 finished: collect at SparkSQLParquetOps.java:23, took 1.863220 s
[Alyssa,null,WrappedArray(3, 9, 15, 20)]
[Ben,red,WrappedArray()]
16/04/02 09:18:30 INFO SparkContext: Invoking stop() from shutdown hook
16/04/02 09:18:30 INFO SparkUI: Stopped Spark web UI at http://192.168.56.1:4040
16/04/02 09:18:30 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
16/04/02 09:18:30 INFO MemoryStore: MemoryStore cleared
16/04/02 09:18:30 INFO BlockManager: BlockManager stopped
16/04/02 09:18:30 INFO BlockManagerMaster: BlockManagerMaster stopped
16/04/02 09:18:30 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
16/04/02 09:18:30 INFO RemoteActorRefProvider$RemotingTerminator: Shutting down remote daemon.
16/04/02 09:18:30 INFO SparkContext: Successfully stopped SparkContext
16/04/02 09:18:30 INFO ShutdownHookManager: Shutdown hook called
16/04/02 09:18:30 INFO ShutdownHookManager: Deleting directory C:\Users\think\AppData\Local\Temp\spark-46e1adfd-4a69-42a8-9b91-24fb8dd8da16
16/04/02 09:18:30 INFO RemoteActorRefProvider$RemotingTerminator: Remote daemon shut down; proceeding with flushing remote transports.
以上内容是王家林老师DT大数据梦工厂《 IMF传奇行动》第62课的学习笔记。
王家林老师是Spark、Flink、Docker、Android技术中国区布道师。Spark亚太研究院院长和首席专家，DT大数据梦工厂创始人，Android软硬整合源码级专家，英语发音魔术师，健身狂热爱好者。
微信公众账号：DT_Spark
联系邮箱18610086859@126.com
电话：18610086859
QQ:1740415547
微信号：18610086859
新浪微博：ilovepains
附：
apache parquet官网http://parquet.apache.org/documentation/latest/ 1/7对parquet的解释：
/ Apache Parquet (http://parquet.apache.org)
Motivation
We created Parquet to make the advantages of compressed, efficient columnar data
representation available to any project in the Hadoop ecosystem.
Parquet is built from the ground up with complex nested data structures in mind, and uses the
record shredding and assembly algorithm (https://github.com/Parquet/parquet-mr/wiki/The-
striping-and-assembly-algorithms-from-the-Dremel-paper) described in the Dremel paper. We
believe this approach is superior to simple flattening of nested name spaces.
Parquet is built to support very efficient compression and encoding schemes. Multiple projects
have demonstrated the performance impact of applying the right compression and encoding
scheme to the data. Parquet allows compression schemes to be specified on a per-column level,
and is future-proofed to allow adding more encodings as they are invented and implemented.
Parquet is built to be used by anyone. The Hadoop ecosystem is rich with data processing
frameworks, and we are not interested in playing favorites. We believe that an efficient, well-
implemented columnar storage substrate should be useful to all frameworks without the cost of
extensive and difficult to set up dependencies.
Modules
The parquet-format project contains format specifications and Thrift definitions of metadata
required to properly read Parquet files.
The parquet-mr project contains multiple sub-modules, which implement the core components of
reading and writing a nested, column-oriented data stream, map this core onto the parquet
format, and provide Hadoop Input/Output Formats, Pig loaders, and other java-based utilities for
interacting with Parquet.
The parquet-compatibility project contains compatibility tests that can be used to verify that
implementations in different languages can read and write each other’s files.
Building
Java resources can be build using mvn package. The current stable version should always be
available from Maven Central.
C++ thrift resources can be generated via make.
Thrift can be also code-genned into any other thrift-supported language.
Releasing
See How to Release (../how-to-release/).
Glossary
Apache Software Foundation
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 2/7
Block (hdfs block): This means a block in hdfs and the meaning is unchanged for
describing this file format. The file format is designed to work well on top of hdfs.
File: A hdfs file that must include the metadata for the file. It does not need to actually
contain the data.
Row group: A logical horizontal partitioning of the data into rows. There is no physical
structure that is guaranteed for a row group. A row group consists of a column chunk for
each column in the dataset.
Column chunk: A chunk of the data for a particular column. These live in a particular row
group and is guaranteed to be contiguous in the file.
Page: Column chunks are divided up into pages. A page is conceptually an indivisible unit
(in terms of compression and encoding). There can be multiple page types which is
interleaved in a column chunk.
Hierarchically, a file consists of one or more row groups. A row group contains exactly one column
chunk per column. Column chunks contain one or more pages.
Unit of parallelization
MapReduce - File/Row Group
IO - Column chunk
Encoding/Compression - Page
File format
This file and the thrift definition should be read together to understand the format.
4-byte magic number "PAR1"
<Column 1 Chunk 1 + Column Metadata>
<Column 2 Chunk 1 + Column Metadata>
...
<Column N Chunk 1 + Column Metadata>
<Column 1 Chunk 2 + Column Metadata>
<Column 2 Chunk 2 + Column Metadata>
...
<Column N Chunk 2 + Column Metadata>
...
<Column 1 Chunk M + Column Metadata>
<Column 2 Chunk M + Column Metadata>
...
<Column N Chunk M + Column Metadata>
File Metadata
4-byte length in bytes of file metadata
4-byte magic number "PAR1"
In the above example, there are N columns in this table, split into M row groups. The file metadata
contains the locations of all the column metadata start locations. More details on what is
contained in the metadata can be found in the thrift files.
Metadata is written after the data to allow for single pass writing.
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 3/7
Readers are expected to first read the file metadata to find all the column chunks they are
interested in. The columns chunks should then be read sequentially.
Metadata
There are three types of metadata: file metadata, column (chunk) metadata and page header
metadata. All thrift structures are serialized using the TCompactProtocol.
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 4/7
Types
The types supported by the file format are intended to be as minimal as possible, with a focus on
how the types effect on disk storage. For example, 16-bit ints are not explicitly supported in the
storage format since they are covered by 32-bit ints with an efficient encoding. This reduces the
complexity of implementing readers and writers for the format. The types are: - BOOLEAN: 1 bit
boolean - INT32: 32 bit signed ints - INT64: 64 bit signed ints - INT96: 96 bit signed ints - FLOAT:
IEEE 32-bit floating point values - DOUBLE: IEEE 64-bit floating point values - BYTE_ARRAY:
arbitrarily long byte arrays.
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 5/7
Logical Types
Logical types are used to extend the types that parquet can be used to store, by specifying how
the primitive types should be interpreted. This keeps the set of primitive types to a minimum and
reuses parquet’s efficient encodings. For example, strings are stored as byte arrays (binary) with
a UTF8 annotation. These annotations define how to further decode and interpret the data.
Annotations are stored as a ConvertedType in the file metadata and are documented in
LogicalTypes.md (https://github.com/Parquet/parquet-format/blob/master/LogicalTypes.md).
Nested Encoding
To encode nested columns, Parquet uses the Dremel encoding with definition and repetition
levels. Definition levels specify how many optional fields in the path for the column are defined.
Repetition levels specify at what repeated field in the path has the value repeated. The max
definition and repetition levels can be computed from the schema (i.e. how much nesting there
is). This defines the maximum number of bits required to store the levels (levels are defined for all
values in the column).
Two encodings for the levels are supported BITPACKED and RLE. Only RLE is now used as it
supersedes BITPACKED.
Nulls
Nullity is encoded in the definition levels (which is run-length encoded). NULL values are not
encoded in the data. For example, in a non-nested schema, a column with 1000 NULLs would be
encoded with run-length encoding (0, 1000 times) for the definition levels and nothing else.
Data Pages
For data pages, the 3 pieces of information are encoded back to back, after the page header.
We have the - definition levels data,
- repetition levels data, - encoded values. The size of specified in the header is for all 3 pieces
combined.
The data for the data page is always required. The definition and repetition levels are optional,
based on the schema definition. If the column is not nested (i.e. the path to the column has length
1), we do not encode the repetition levels (it would always have the value 1). For data that is
required, the definition levels are skipped (if encoded, it will always have the value of the max
definition level).
For example, in the case where the column is non-nested and required, the data in the page is
only the encoded values.
The supported encodings are described in Encodings.md (https://github.com/Parquet/parquet-
format/blob/master/Encodings.md)
Column chunks
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 6/7
Column chunks are composed of pages written back to back. The pages share a common header
and readers can skip over page they are not interested in. The data for the page follows the
header and can be compressed and/or encoded. The compression and encoding is specified in
the page metadata.
Checksumming
Data pages can be individually checksummed. This allows disabling of checksums at the HDFS
file level, to better support single row lookups.
Error recovery
If the file metadata is corrupt, the file is lost. If the column metdata is corrupt, that column chunk is
lost (but column chunks for this column in other row groups are okay). If a page header is
corrupt, the remaining pages in that chunk are lost. If the data within a page is corrupt, that page
is lost. The file will be more resilient to corruption with smaller row groups.
Potential extension: With smaller row groups, the biggest issue is placing the file metadata at the
end. If an error happens while writing the file metadata, all the data written will be unreadable.
This can be fixed by writing the file metadata every Nth row group.
Each file metadata would be cumulative and include all the row groups written so far. Combining
this with the strategy used for rc or avro files using sync markers, a reader could recover partially
written files.
Separating metadata and column data.
The format is explicitly designed to separate the metadata from the data. This allows splitting
columns into multiple files, as well as having a single metadata file reference multiple parquet
files.
Configurations
Row group size: Larger row groups allow for larger column chunks which makes it possible
to do larger sequential IO. Larger groups also require more buffering in the write path (or a
two pass write). We recommend large row groups (512MB - 1GB). Since an entire row
group might need to be read, we want it to completely fit on one HDFS block. Therefore,
HDFS block sizes should also be set to be larger. An optimized read setup would be: 1GB
row groups, 1GB HDFS block size, 1 HDFS block per HDFS file.
Data page size: Data pages should be considered indivisible so smaller data pages allow
for more fine grained reading (e.g. single row lookup). Larger page sizes incur less space
overhead (less page headers) and potentially less parsing overhead (processing
headers). Note: for sequential scans, it is not expected to read a page at a time; this is not
the IO chunk. We recommend 8KB for page sizes.
Extensibility
2016/3/23 Apache Parquet
http://parquet.apache.org/documentation/latest/ 7/7
There are many places in the format for compatible extensions: - File Version: The file metadata
contains a version. - Encodings: Encodings are specified by enum and more can be added in the
future.
- Page types: Additional page types can be added and safely skipped.
Copyright 2014 Apache Software Foundation (http://www.apache.org/). Licensed under the
Apache License v2.0 (http://www.apache.org/licenses/). Apache Parquet and the Apache feather
logo are trademarks of The Apache Software Foundation.
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