I m working on PIG script which performs heavy duty data processing on raw transactions and come up with various transaction patterns.
Say one of pattern is - find all accounts who received cross border transactions in a day (with total transaction and amount of transactions).
My expected output should be two data files
1) Rollup data - like account A1 received 50 transactions from country AU.
2) Raw transactions - all above 50 transactions for A1.
My PIG script is currently creating output data source in following format
Account Country TotalTxns RawTransactions
A1 AU 50 [(Txn1), (Txn2), (Txn3)....(Txn50)]
A2 JP 30 [(Txn1), (Txn2)....(Txn30)]
Now question here is, when I get this data out of Hadoop system (to some DB) I want to establish link between my rollup record (A1, AU, 50) with all 50 raw transactions (like ID 1 for rollup record used as foreign key for all 50 associated Txns).
I understand Hadoop being distributed should not be used for assigning IDs, but are there any options where i can assign non-unique Ids (no need to be sequential) or some other way to link this data?
EDIT (after using Enumerate from DataFu)
here is the PIG script
register /UDF/datafu-0.0.8.jar
define Enumerate datafu.pig.bags.Enumerate('1');
data_txn = LOAD './txndata' USING PigStorage(',') AS (txnid:int, sndr_acct:int,sndr_cntry:chararray, rcvr_acct:int, rcvr_cntry:chararray);
data_txn1 = GROUP data_txn ALL;
data_txn2 = FOREACH data_txn1 GENERATE flatten(Enumerate(data_txn));
dump data_txn2;
after running this, I am getting
ERROR org.apache.pig.tools.pigstats.SimplePigStats - ERROR 2997: Unable to recreate exception from backed error: java.lang.NullPointerException
at datafu.pig.bags.Enumerate.enumerateBag(Enumerate.java:89)
at datafu.pig.bags.Enumerate.accumulate(Enumerate.java:104)
....
I often assign random ids in Hadoop jobs. You just need to ensure you generate ids which contain a sufficient number of random bits to ensure the probability of collisions is sufficiently small (http://en.wikipedia.org/wiki/Birthday_problem).
As a rule of thumb I use 3*log(n) random bits where n = # of ids that need to be generated.
In many cases Java's UUID.randomUUID() will be sufficient.
http://en.wikipedia.org/wiki/Universally_unique_identifier#Random_UUID_probability_of_duplicates
What is unique in your rows? It appears that account ID and country code are what you have grouped by in your Pig script, so why not make a composite key with those? Something like
CONCAT(CONCAT(account, '-'), country)
Of course, you could write a UDF to make this more elegant. If you need a numeric ID, try writing a UDF which will create the string as above, and then call its hashCode() method. This will not guarantee uniqueness of course, but you said that was all right. You can always construct your own method of translating a string to an integer that is unique.
But that said, why do you need a single ID key? If you want to join the fields of two tables later, you can join on more than one field at a time.
DataFu had a bug in Enumerate which was fixed in 0.0.9, so use 0.0.9 or later.
In case when your IDs are numbers and you can not use UUID or other string based IDs.
There is a DataFu library of UDFs by LinkedIn (DataFu) with a very useful UDF Enumerate. So what you can do is to group all records into a bag and pass the bag to the Enumerate. Here is the code from top of my head:
register jar with UDF with Enumerate UDF
inpt = load '....' ....;
allGrp = group inpt all;
withIds = foreach allGrp generate flatten(Enumerate(inpt));
Related
My requirement is to write a nested entity(Array of POJO objects) from Java to Hbase and to read them as individual records in Hive.
(i,e) while writing from Java, its just a single string(Array). But from hive, the array represents the table as a whole. So the hive should have the individual elements of the array as individual records in it.
Any help on this will be appreciated.
Thanks,
GK
Perhaps you should take a look to Hive UDTF functions like explode, depending on what you store and what you need to retrieve they may work for you but be noticed they have some important limitations:
No other expressions are allowed in SELECT SELECT pageid, explode(adid_list) AS myCol... is not supported
UDTF's can't be nested SELECT explode(explode(adid_list)) AS myCol... is not supported
GROUP BY / CLUSTER BY / DISTRIBUTE BY / SORT BY is not supported SELECT explode(adid_list) AS myCol ... GROUP BY myCol is not
supported
If standard UDTFs don't fit your case and you're in the mood, you can also do this:
Store each item of your array as a json string in a different column: i0, i1, i2 ... iN
Write your own UDTF function to process each row columns and emit 1 row per column.
IMHO, I'll just write one row per element of the array, appending to the rowkey the index of each array item, it will be faster when processing the data and you'll have a lot less headaches. You shouldn't worry about writing billions of rows if that's the case.
Current application use JPA to auto generate table/entity id. Now a requirement wants to get a query to manually insert data in to the database using SQL queries
So the questions are:
Is it worth to create a sequence in this schema just for this little requirement?
If answer to 1 is no, then what could be a plan b?
Yes. A sequence is trivial - why would you not do it?
N/A
Few ways:
Use a UUID. UUIDs are pseudo-random, large alphanumeric strings which are guaranteed to be unique once generated.
Does the data have something unique? Like a timestamp, or IP address, etc? If so, use that
Combination of current timestamp + some less unique value in the data
Combination of current timestamp + some integer i that you keep incrementing
There are others (including generating a checksum, custom random numbers instead of UUIDs, etc) - but those have the possibility of overlaps, so not mentioning them.
Edit: Minor clarifications
Are you just doing a single data load into an empty table, and there are no other users concurrently inserting data? If so, you can just use ROWNUM to generate the IDs starting from 1, e.g.
INSERT INTO mytable
SELECT ROWNUM AS ID
,etc AS etc
FROM ...
I have always read that Cassandra is good if your application changes frequently and features are added frequently.
That makes sense, since you don't have any fixed schema, you can add columns to rows to suffice your needs, instead of running an ALTER TABLE query which may freeze your database for hours for very large tables.
However I have an hypotetical problem which I'm not able to solve.
Let's say I have:
CREATE COLUMN FAMILY Students
with comparator='CompositeType(UTF8Type,UTF8Type),
and key_validation_class=UUIDType;
Each student has some generic column (you know, meta:username, meta:password, meta:surname, etc), plus each student may follow N courses. This N-N relationship is resolved using denormalization, adding N columns to each Student (course:ID1, course:ID2).
On the other side, I may have a Courses CF, where each row is contains all of the following Students UUIDs.
So I can ask "which courses are followed by XXX" and "which students follow course YYY".
The problem is: what if I didn't create the second column family? Maybe at the time when the application was built, getting the students following a specific course wasn't a requirement.
This is a simple example, but I believe it's quite common. "With Cassandra you plan CFs in terms of queries instead of relationships". I need that query now, while at first it wasn't needed.
Given a table of students with thousands of entries, how would you fill the Courses CF? Is this a job for Hadoop, Pig or Hive (I never touched any of those, just guessing).
Pig (which uses the Hadoop integration) is actually perfect for this type of work, because you can not only read but also write data back into Cassandra using CassandraStorage. It gives you the parallel processing capability to do the job with minimal time and overhead. Otherwise the alternative is to write something to do the extraction yourself, then write the new CF.
Here is a Pig example that computes averages from a set of data in one CF and outputs them to another:
rows = LOAD 'cassandra://HadoopTest/TestInput' USING CassandraStorage() AS (key:bytearray,cols:bag{col:tuple(name:chararray,value)});
columns = FOREACH rows GENERATE flatten(cols) AS (name,value);
grouped = GROUP columns BY name;
vals = FOREACH grouped GENERATE group, columns.value AS values;
avgs = FOREACH vals GENERATE group, 'Pig_Average' AS name, (long)SUM(values.value)/COUNT(values.value) AS average;
cass_group = GROUP avgs BY group;
cass_out = FOREACH cass_group GENERATE group, avgs.(name, average);
STORE cass_out INTO 'cassandra://HadoopTest/TestOutput' USING CassandraStorage();
If you use the existing cassandra file, you would have to unwind the data. Since NOSQL files are unidirectional this could be a very time consuming operation in Cassandra itself. The data would have to be sorted in the opposite order from the first file. Frankly I believe that you would have to go back to the original data that was used to populate the first file and populate this new file from that.
Suppose I have two key-value data sets--Data Sets A and B, let's call them. I want to update all the data in Set A with data from Set B where the two match on keys.
Because I'm dealing with such large quantities of data, I'm using Hadoop to MapReduce. My concern is that to do this key matching between A and B, I need to load all of Set A (a lot of data) into the memory of every mapper instance. That seems rather inefficient.
Would there be a recommended way to do this that doesn't require repeating the work of loading in A every time?
Some pseudcode to clarify what I'm currently doing:
Load in Data Set A # This seems like the expensive step to always be doing
Foreach key/value in Data Set B:
If key is in Data Set A:
Update Data Seta A
According to the documentation, the MapReduce framework includes the following steps:
Map
Sort/Partition
Combine (optional)
Reduce
You've described one way to perform your join: loading all of Set A into memory in each Mapper. You're correct that this is inefficient.
Instead, observe that a large join can be partitioned into arbitrarily many smaller joins if both sets are sorted and partitioned by key. MapReduce sorts the output of each Mapper by key in step (2) above. Sorted Map output is then partitioned by key, so that one partition is created per Reducer. For each unique key, the Reducer will receive all values from both Set A and Set B.
To finish your join, the Reducer needs only to output the key and either the updated value from Set B, if it exists; otherwise, output the key and the original value from Set A. To distinguish between values from Set A and Set B, try setting a flag on the output value from the Mapper.
All of the answers posted so far are correct - this should be a Reduce-side join... but there's no need to reinvent the wheel! Have you considered Pig, Hive, or Cascading for this? They all have joins built-in, and are fairly well optimized.
This video tutorial by Cloudera gives a great description of how to do a large-scale Join through MapReduce, starting around the 12 minute mark.
Here are the basic steps he lays out for joining records from file B onto records from file A on key K, with pseudocode. If anything here isn't clear, I'd suggest watching the video as he does a much better job explaining it than I can.
In your Mapper:
K from file A:
tag K to identify as Primary Key
emit <K, value of K>
K from file B:
tag K to identify as Foreign Key
emit <K, record>
Write a Sorter and Grouper which will ignore the PK/FK tagging, so that your records are sent to the same Reducer regardless of whether they are a PK record or a FK record and are grouped together.
Write a Comparator which will compare the PK and FK keys and send the PK first.
The result of this step will be that all records with the same key will be sent to the same Reducer and be in the same set of values to be reduced. The record tagged with PK will be first, followed by all records from B which need to be joined. Now, the Reducer:
value_of_PK = values[0] // First value is the value of your primary key
for value in values[1:]:
value.replace(FK,value_of_PK) // Replace the foreign key with the key's value
emit <key, value>
The result of this will be file B, with all occurrences of K replaced by the value of K in file A. You can also extend this to effect a full inner join, or to write out both files in their entirety for direct database storage, but those are pretty trivial modifications once you get this working.
I am trying to extract some part of string and store it to hbase in columns.
Files Content :
msgType1 Person xyz has opened Internet:www.google.com from IP:192.123.123.123 for duration 00:15:00
msgType2 Person xyz denied for opening Internet:202.x.x.x from IP:192.123.123.123 reason:unautheticated
msgType1 Person xyz has opened Internet:202.x.x.x from IP:192.123.123.123 for duration 00:15:00
pattern of messages corresponding to msgType is fixed. Now i am trying to store person name, destination , source , duration etc in hbase.
I am trying to to wrtie script in PIG to do this task.
But i am stuck at extracting part.(extracting IP or website name from 'Internet:202.x.x.x' token inside string).
I tried Regular expression but its not working for me. Regex alway throw this error :
ERROR 1045: Could not infer the matching function for org.apache.pig.builtin.REGEX_EXTRACT as multiple or none of them fit. Please use an explicit cast.
is there any other way to extract these value and store it to hbase in PIG or other than PIG?
How do you use the REGEX_EXTRACT function ? Have you seen the REGEX_EXTRACT_ALL function ? According to the documentation (http://pig.apache.org/docs/r0.9.2/func.html#regex-extract-all), it should be like this :
test = LOAD 'test.csv' USING org.apache.pig.builtin.PigStorage(',') AS (key:chararray, value:chararray);
test = FOREACH test GENERATE FLATTEN(REGEX_EXTRACT_ALL (value, '(\\S+):(\\S+)')) as (match1:chararray, match2:chararray);
DUMP test;
My file is like that :
1,a:b
2,c:d
3,
I know it's easy to be lazy and not take the step, but you really should use a user-defined function here. Pig is good as a data flow language and not much else, so in order to get the full power out of it, you are going to need to use a lot of UDFs to go through text and do more complicated operations.
The UDF will take a single string as a parameter, then return a tuple that represents (person, destination, source, duration). To use it, you'll do:
A = LOAD ...
...
B = FOREACH A GENERATE MyParseUDF(logline);
...
STORE B INTO ...
You didn't mention what your HBase row key was, but be sure that's the first element in the relation before storing it.