A simple module that counts messages received, using Spring Boot metrics abstraction.
The counter sink has the following options:
- name
- The name of the counter to increment. (String, default:
counts) - nameExpression
- A SpEL expression (against the incoming Message) to derive the name of the counter to increment. (String, default: ``)
- store
- The name of a store used to store the counter. (String, default:
memory, possible values:memory,redis)
If you do not have Hadoop installed, you can install Hadoop as described in our separate guide. Spring XD supports 4 Hadoop distributions, see using Hadoop for more information on how to start Spring XD to target a specific distribution.
Once Hadoop is up and running, you can then use the hdfs sink when creating a stream
dataflow:> stream create --name myhdfsstream1 --definition "time | hdfs" --deploy
In the above example, we’ve scheduled time source to automatically send ticks to hdfs once in every second. If you wait a little while for data to accumuluate you can then list can then list the files in the hadoop filesystem using the shell’s built in hadoop fs commands. Before making any access to HDFS in the shell you first need to configure the shell to point to your name node. This is done using the hadoop config command.
dataflow:>hadoop config fs --namenode hdfs://localhost:8020
In this example the hdfs protocol is used but you may also use the webhdfs protocol. Listing the contents in the output directory (named by default after the stream name) is done by issuing the following command.
dataflow:>hadoop fs ls /xd/myhdfsstream1 Found 1 items -rw-r--r-- 3 jvalkealahti supergroup 0 2013-12-18 18:10 /xd/myhdfsstream1/myhdfsstream1-0.txt.tmp
While the file is being written to it will have the tmp suffix. When the data written exceeds the rollover size (default 1GB) it will be renamed to remove the tmp suffix. There are several options to control the in use file file naming options. These are --inUsePrefix and --inUseSuffix set the file name prefix and suffix respectfully.
When you destroy a stream
dataflow:>stream destroy --name myhdfsstream1
and list the stream directory again, in use file suffix doesn’t exist anymore.
dataflow:>hadoop fs ls /xd/myhdfsstream1 Found 1 items -rw-r--r-- 3 jvalkealahti supergroup 380 2013-12-18 18:10 /xd/myhdfsstream1/myhdfsstream1-0.txt
To list the list the contents of a file directly from a shell execute the hadoop cat command.
dataflow:> hadoop fs cat /xd/myhdfsstream1/myhdfsstream1-0.txt 2013-12-18 18:10:07 2013-12-18 18:10:08 2013-12-18 18:10:09 ...
In the above examples we didn’t yet go through why the file was written in a specific directory and why it was named in this specific way. Default location of a file is defined as /xd/<stream name>/<stream name>-<rolling part>.txt. These can be changed using options --directory and --fileName respectively. Example is shown below.
dataflow:>stream create --name myhdfsstream2 --definition "time | hdfs --directory=/xd/tmp --fileName=data" --deploy dataflow:>stream destroy --name myhdfsstream2 dataflow:>hadoop fs ls /xd/tmp Found 1 items -rw-r--r-- 3 jvalkealahti supergroup 120 2013-12-18 18:31 /xd/tmp/data-0.txt
It is also possible to control the size of a files written into HDFS. The --rollover option can be used to control when file currently being written is rolled over and a new file opened by providing the rollover size in bytes, kilobytes, megatypes, gigabytes, and terabytes.
dataflow:>stream create --name myhdfsstream3 --definition "time | hdfs --rollover=100" --deploy dataflow:>stream destroy --name myhdfsstream3 dataflow:>hadoop fs ls /xd/myhdfsstream3 Found 3 items -rw-r--r-- 3 jvalkealahti supergroup 100 2013-12-18 18:41 /xd/myhdfsstream3/myhdfsstream3-0.txt -rw-r--r-- 3 jvalkealahti supergroup 100 2013-12-18 18:41 /xd/myhdfsstream3/myhdfsstream3-1.txt -rw-r--r-- 3 jvalkealahti supergroup 100 2013-12-18 18:41 /xd/myhdfsstream3/myhdfsstream3-2.txt
Shortcuts to specify sizes other than bytes are written as --rollover=64M, --rollover=512G or --rollover=1T.
The stream can also be compressed during the write operation. Example of this is shown below.
dataflow:>stream create --name myhdfsstream4 --definition "time | hdfs --codec=gzip" --deploy dataflow:>stream destroy --name myhdfsstream4 dataflow:>hadoop fs ls /xd/myhdfsstream4 Found 1 items -rw-r--r-- 3 jvalkealahti supergroup 80 2013-12-18 18:48 /xd/myhdfsstream4/myhdfsstream4-0.txt.gzip
From a native os shell we can use hadoop’s fs commands and pipe data into gunzip.
# bin/hadoop fs -cat /xd/myhdfsstream4/myhdfsstream4-0.txt.gzip | gunzip 2013-12-18 18:48:10 2013-12-18 18:48:11 ...
Often a stream of data may not have a high enough rate to roll over files frequently, leaving the file in an opened state. This prevents users from reading a consistent set of data when running mapreduce jobs. While one can alleviate this problem by using a small rollover value, a better way is to use the idleTimeout option that will automatically close the file if there was no writes during the specified period of time. This feature is also useful in cases where burst of data is written into a stream and you’d like that data to become visible in HDFS.
![[Note]](images/note.png) | Note |
|---|---|
The |
dataflow:> stream create --name myhdfsstream5 --definition "http --port=8000 | hdfs --rollover=20 --idleTimeout=10000" --deploy
In the above example we changed a source to http order to control what we write into a hdfs sink. We defined a small rollover size and a timeout of 10 seconds. Now we can simply post data into this stream via source end point using a below command.
dataflow:> http post --target http://localhost:8000 --data "hello"
If we repeat the command very quickly and then wait for the timeout we should be able to see that some files are closed before rollover size was met and some were simply rolled because of a rollover size.
dataflow:>hadoop fs ls /xd/myhdfsstream5 Found 4 items -rw-r--r-- 3 jvalkealahti supergroup 12 2013-12-18 19:02 /xd/myhdfsstream5/myhdfsstream5-0.txt -rw-r--r-- 3 jvalkealahti supergroup 24 2013-12-18 19:03 /xd/myhdfsstream5/myhdfsstream5-1.txt -rw-r--r-- 3 jvalkealahti supergroup 24 2013-12-18 19:03 /xd/myhdfsstream5/myhdfsstream5-2.txt -rw-r--r-- 3 jvalkealahti supergroup 18 2013-12-18 19:03 /xd/myhdfsstream5/myhdfsstream5-3.txt
Files can be automatically partitioned using a partitionPath expression. If we create a stream with idleTimeout and partitionPath with simple format yyyy/MM/dd/HH/mm we should see writes ending into its own files within every minute boundary.
dataflow:>stream create --name myhdfsstream6 --definition "time|hdfs --idleTimeout=10000 --partitionPath=dateFormat('yyyy/MM/dd/HH/mm')" --deployLet a stream run for a short period of time and list files.
dataflow:>hadoop fs ls --recursive true --dir /xd/myhdfsstream6 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:42 /xd/myhdfsstream6/2014 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:42 /xd/myhdfsstream6/2014/05 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:42 /xd/myhdfsstream6/2014/05/28 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:45 /xd/myhdfsstream6/2014/05/28/09 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:43 /xd/myhdfsstream6/2014/05/28/09/42 -rw-r--r-- 3 jvalkealahti supergroup 140 2014-05-28 09:43 /xd/myhdfsstream6/2014/05/28/09/42/myhdfsstream6-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:44 /xd/myhdfsstream6/2014/05/28/09/43 -rw-r--r-- 3 jvalkealahti supergroup 1200 2014-05-28 09:44 /xd/myhdfsstream6/2014/05/28/09/43/myhdfsstream6-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 09:45 /xd/myhdfsstream6/2014/05/28/09/44 -rw-r--r-- 3 jvalkealahti supergroup 1200 2014-05-28 09:45 /xd/myhdfsstream6/2014/05/28/09/44/myhdfsstream6-0.txt
Partitioning can also be based on defined lists. In a below example we simulate feeding data by using a time and a transform elements. Data passed to hdfs sink has a content APP0:foobar, APP1:foobar, APP2:foobar or APP3:foobar.
dataflow:>stream create --name myhdfsstream7 --definition "time | transform --expression=\"'APP'+T(Math).round(T(Math).random()*3)+':foobar'\" | hdfs --idleTimeout=10000 --partitionPath=path(dateFormat('yyyy/MM/dd/HH'),list(payload.split(':')[0],{{'0TO1','APP0','APP1'},{'2TO3','APP2','APP3'}}))" --deployLet the stream run few seconds, destroy it and check what got written in those partitioned files.
dataflow:>stream destroy --name myhdfsstream7 Destroyed stream 'myhdfsstream7' dataflow:>hadoop fs ls --recursive true --dir /xd drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014/05 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28/19 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28/19/0TO1_list -rw-r--r-- 3 jvalkealahti supergroup 108 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28/19/0TO1_list/myhdfsstream7-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28/19/2TO3_list -rw-r--r-- 3 jvalkealahti supergroup 180 2014-05-28 19:24 /xd/myhdfsstream7/2014/05/28/19/2TO3_list/myhdfsstream7-0.txt dataflow:>hadoop fs cat /xd/myhdfsstream7/2014/05/28/19/0TO1_list/myhdfsstream7-0.txt APP1:foobar APP1:foobar APP0:foobar APP0:foobar APP1:foobar
Partitioning can also be based on defined ranges. In a below example we simulate feeding data by using a time and a transform elements. Data passed to hdfs sink has a content ranging from APP0 to APP15. We simple parse the number part and use it to do a partition with ranges {3,5,10}.
dataflow:>stream create --name myhdfsstream8 --definition "time | transform --expression=\"'APP'+T(Math).round(T(Math).random()*15)\" | hdfs --idleTimeout=10000 --partitionPath=path(dateFormat('yyyy/MM/dd/HH'),range(T(Integer).parseInt(payload.substring(3)),{3,5,10}))" --deployLet the stream run few seconds, destroy it and check what got written in those partitioned files.
dataflow:>stream destroy --name myhdfsstream8 Destroyed stream 'myhdfsstream8' dataflow:>hadoop fs ls --recursive true --dir /xd drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/10_range -rw-r--r-- 3 jvalkealahti supergroup 16 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/10_range/myhdfsstream8-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/3_range -rw-r--r-- 3 jvalkealahti supergroup 35 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/3_range/myhdfsstream8-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/5_range -rw-r--r-- 3 jvalkealahti supergroup 5 2014-05-28 19:34 /xd/myhdfsstream8/2014/05/28/19/5_range/myhdfsstream8-0.txt dataflow:>hadoop fs cat /xd/myhdfsstream8/2014/05/28/19/3_range/myhdfsstream8-0.txt APP3 APP3 APP1 APP0 APP1 dataflow:>hadoop fs cat /xd/myhdfsstream8/2014/05/28/19/5_range/myhdfsstream8-0.txt APP4 dataflow:>hadoop fs cat /xd/myhdfsstream8/2014/05/28/19/10_range/myhdfsstream8-0.txt APP6 APP15 APP7
Partition using a dateFormat can be based on content itself. This is a good use case if old log files needs to be processed where partitioning should happen based on timestamp of a log entry. We create a fake log data with a simple date string ranging from 1970-01-10 to 1970-01-13.
dataflow:>stream create --name myhdfsstream9 --definition "time | transform --expression=\"'1970-01-'+1+T(Math).round(T(Math).random()*3)\" | hdfs --idleTimeout=10000 --partitionPath=path(dateFormat('yyyy/MM/dd/HH',payload,'yyyy-MM-DD'))" --deployLet the stream run few seconds, destroy it and check what got written in those partitioned files. If you see the partition paths, those are based on year 1970, not present year.
dataflow:>stream destroy --name myhdfsstream9 Destroyed stream 'myhdfsstream9' dataflow:>hadoop fs ls --recursive true --dir /xd drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970/01 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970/01/10 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/10/00 -rw-r--r-- 3 jvalkealahti supergroup 44 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/10/00/myhdfsstream9-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970/01/11 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/11/00 -rw-r--r-- 3 jvalkealahti supergroup 99 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/11/00/myhdfsstream9-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970/01/12 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/12/00 -rw-r--r-- 3 jvalkealahti supergroup 44 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/12/00/myhdfsstream9-0.txt drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:56 /xd/myhdfsstream9/1970/01/13 drwxr-xr-x - jvalkealahti supergroup 0 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/13/00 -rw-r--r-- 3 jvalkealahti supergroup 55 2014-05-28 19:57 /xd/myhdfsstream9/1970/01/13/00/myhdfsstream9-0.txt dataflow:>hadoop fs cat /xd/myhdfsstream9/1970/01/10/00/myhdfsstream9-0.txt 1970-01-10 1970-01-10 1970-01-10 1970-01-10
The hdfs sink has the following options:
- closeTimeout
- timeout in ms, regardless of activity, after which file will be automatically closed (long, default:
0) - codec
- compression codec alias name (gzip, snappy, bzip2, lzo, or slzo) (String, default: ``)
- directory
- where to output the files in the Hadoop FileSystem (String, default:
/tmp/hdfs-sink) - fileExtension
- the base filename extension to use for the created files (String, default:
txt) - fileName
- the base filename to use for the created files (String, default:
<stream name>) - fileOpenAttempts
- maximum number of file open attempts to find a path (int, default:
10) - fileUuid
- whether file name should contain uuid (boolean, default:
false) - fsUri
- the URI to use to access the Hadoop FileSystem (String, default:
${spring.hadoop.fsUri}) - idleTimeout
- inactivity timeout in ms after which file will be automatically closed (long, default:
0) - inUsePrefix
- prefix for files currently being written (String, default: ``)
- inUseSuffix
- suffix for files currently being written (String, default:
.tmp) - overwrite
- whether writer is allowed to overwrite files in Hadoop FileSystem (boolean, default:
false) - partitionPath
- a SpEL expression defining the partition path (String, default: ``)
- rollover
- threshold in bytes when file will be automatically rolled over (String, default:
1G)
| Note |
|---|---|
In the context of the |
SpEL expression is evaluated against a Spring Messaging Message passed internally into a HDFS writer. This allows expression to use headers and payload from that message. While you could do a custom processing within a stream and add custom headers, timestamp is always going to be there. Data to be written is then available in a payload.
Using a payload simply returns whatever is currently being written. Access to headers is via headers property. Any other property is automatically resolved from headers if found. For example headers.timestamp is equivalent to timestamp.
Addition to a normal SpEL functionality, few custom methods has been added to make it easier to build partition paths. These custom methods can be used to work with a normal partition concepts like date formatting, lists, ranges and hashes.
path(String... paths)
Concatenates paths together with a delimiter /. This method can be used to make the expression less verbose than using a native SpEL functionality to combine path parts together. To create a path part1/part2, expression 'part1' + '/' + 'part2' is equivalent to path('part1','part2').
Parameters
- paths
- Any number of path parts
Return Value. Concatenated value of paths delimited with /.
dateFormat(String pattern) dateFormat(String pattern, Long epoch) dateFormat(String pattern, Date date) dateFormat(String pattern, String datestring) dateFormat(String pattern, String datestring, String dateformat)
Creates a path using date formatting. Internally this method delegates into SimpleDateFormat and needs a Date and a pattern. On default if no parameter used for conversion is given, timestamp is expected. Effectively dateFormat('yyyy') equals to dateFormat('yyyy', timestamp) or dateFormat('yyyy', headers.timestamp).
Method signature with three parameters can be used to create a custom Date object which is then passed to SimpleDateFormat conversion using a dateformat pattern. This is useful in use cases where partition should be based on a date or time string found from a payload content itself. Default dateformat pattern if omitted is yyyy-MM-dd.
Parameters
- pattern
- Pattern compatible with
SimpleDateFormatto produce a final output. - epoch
- Timestamp as
Longwhich is converted into aDate. - date
- A
Dateto be formatted. - dateformat
- Secondary pattern to convert
datestringinto aDate. - datestring
Dateas aString
Return Value. A path part representation which can be a simple file or directory name or a directory structure.
list(Object source, List<List<Object>> lists)
Creates a partition path part by matching a source against a lists denoted by lists.
Lets assume that data is being written and it’s possible to extrace an appid either from headers or payload. We can automatically do a list based partition by using a partition method list(headers.appid,{{'1TO3','APP1','APP2','APP3'},{'4TO6','APP4','APP5','APP6'}}). This method would create three partitions, 1TO3_list, 4TO6_list and list. Latter is used if no match is found from partition lists passed to lists.
Parameters
- source
- An
Objectto be matched againstlists. - lists
- A definition of list of lists.
Return Value. A path part prefixed with a matched key i.e. XXX_list or list if no match.
range(Object source, List<Object> list)
Creates a partition path part by matching a source against a list denoted by list using a simple binary search.
The partition method takes a source as first argument and list as a second argument. Behind the scenes this is using jvm’s binarySearch which works on an Object level so we can pass in anything. Remember that meaningful range match only works if passed in Object and types in list are of same type like Integer. Range is defined by a binarySearch itself so mostly it is to match against an upper bound except the last range in a list. Having a list of {1000,3000,5000} means that everything above 3000 will be matched with 5000. If that is an issue then simply adding Integer.MAX_VALUE as last range would overflow everything above 5000 into a new partition. Created partitions would then be 1000_range, 3000_range and 5000_range.
Parameters
- source
- An
Objectto be matched againstlist. - list
- A definition of list.
Return Value. A path part prefixed with a matched key i.e. XXX_range.
hash(Object source, int bucketcount)
Creates a partition path part by calculating hashkey using source`s hashCode and bucketcount. Using a partition method hash(timestamp,2) would then create partitions named 0_hash, 1_hash and 2_hash. Number suffixed with _hash is simply calculated using Object.hashCode() % bucketcount.
Parameters
- source
- An
ObjectwhichhashCodewill be used. - bucketcount
- A number of buckets
Return Value. A path part prefixed with a hash key i.e. XXX_hash.
Probably the simplest option for a sink is just to log the data. The log sink uses the application logger to output the data for inspection. The log level is set to WARN and the logger name is created from the stream name. To create a stream using a log sink you would use a command like
dataflow:> stream create --name mylogstream --definition "http --port=8000 | log" --deploy
You can then try adding some data. We’ve used the http source on port 8000 here, so run the following command to send a message
dataflow:> http post --target http://localhost:8000 --data "hello"
and you should see the following output in the XD container console.
13/06/07 16:12:18 INFO Received: hello
Redis sink can be used to ingest data into redis store. You can choose queue, topic or key with selcted collection type to point to a specific data store.
For example,
dataflow:>stream create store-into-redis --definition "http | redis --queue=myList" --deploy dataflow:>Created and deployed new stream 'store-into-redis'
The redis sink has the following options:
- topicExpression
- a SpEL expression to use for topic (String, no default)
- queueExpression
- a SpEL expression to use for queue (String, no default)
- keyExpression
- a SpEL expression to use for keyExpression (String, no default)
- key
- name for the key (String, no default)
- queue
- name for the queue (String, no default)
- topic
- name for the topic (String, no default)