I am reading json files from GCS and I have to load data into different BigQuery tables. These file may have multiple records for same customer with different timestamp. I have to pick latest among them for each customer. I am planning to achieve as below
Read files
Group by customer id
Apply DoFn to compare timestamp of records in each group and have only latest one from them
Flat it, convert to table row insert into BQ.
But I am unable to proceed with step 1. I see GroupByKey.create() but unable to make it use customer id as key.
I am implementing using JAVA. Any suggestions would be of great help. Thank you.
Before you GroupByKey you need to have your dataset in key-value pairs. It would be good if you had shown some of your code, but without knowing much, you'd do the following:
PCollection<JsonObject> objects = p.apply(FileIO.read(....)).apply(FormatData...)
// Once we have the data in JsonObjects, we key by customer ID:
PCollection<KV<String, Iterable<JsonObject>>> groupedData =
objects.apply(MapElements.via(elm -> KV.of(elm.getString("customerId"), elm)))
.apply(GroupByKey.create())
Once that's done, you can check timestamps and discard all bot the most recent as you were thinking.
Note that you will need to set coders, etc - if you get stuck with that we can iterate.
As a hint / tip, you can consider this example of a Json Coder.
Related
I was asked to create control table with Informatica. I am a newbie and do not have much knowledge about it. I saw the same kind of stuff in my previous project but don't know the way to create a mapplet for that. So the requirement is that I have to create a mapplet which has the following columns:
-mapping_name
-session_name
-last_run_date
--source count
--target count
--status
So what happens is
Example: We executed a workflow with a particular mapping last week.
Now after 1 week we are executing the same mapping.
The requirement is that we should be fetching only those records which fall in this particular time frame(i.e from previous run to the current run). This is something I do not know.
Can you please help me out? I can provide furthur details if required.
There is a solution provided in below link but it doesnt use mapplet.
See, if you want to use mapplet, you wont get 'status' attribute and mapplet approach can be difficult to implement for all mappings.
You can use this link to gather statistics as well.
http://powercenternotes.blogspot.com/2014/01/an-etl-framework-for-operational.html
Now, regarding your other requirement, it seems to me to be an issue with incremental extract. So, you need to store the date parameter when you ran your flow last - into a DB table or flat file.
Use that as reference and pull anything greater than that date.
Mapplet - We used this approach earlier to gather statistics. But this is difficult because you need to add this mapplet + a reusable generic target to capture stats.
Input -
Type_of_data- (this can be source, target)
unique_key - (unique key of the mapping)
MappingName - $PMMappingName
SessionName - $PMSessionName
Aggregator -
i/p-
Type_of_data
unique_key
MappingName group by
SessionName group by
o/p-
count_row = COUNT(*)
Output -
Type_of_data
MappingName
SessionName
count_row
Use a reusable generic target to capture all the rows. You need to add one set after each source, one set before each target. The approach in the link is better i think.
I have an existing HANA warehouse which was built without create/update timestamps. I need to generate a number of nightly batch delta files to send to another platform. My problem is how to detect which records are new or changed so that I can capture those records within the replication process.
Is there a way to use HANA's built-in features to detect new/changed records?
SAP HANA does not provide a general change data capture interface for tables (up to current version HANA 2 SPS 02).
That means, to detect "changed records since a given point in time" some other approach has to be taken.
Depending on the information in the tables different options can be used:
if a table explicitly contains a reference to the last change time, this can be used
if a table has guaranteed update characteristics (e.g. no in-place update and monotone ID values), this could be used. E.g.
read all records where ID is larger than the last processed ID
if the table does not provide intrinsic information about change time then one could maintain a copy of the table that contains
only the records processed so far. This copy can then be used to
compare the current table and compute the difference. SAP HANA's
Smart Data Integration (SDI) flowgraphs support this approach.
In my experience, efforts to try "save time and money" on this seemingly simple problem of a delta load usually turn out to be more complex, time-consuming and expensive than using the corresponding features of ETL tools.
It is possible to create a Log table and organize columns according to your needs so that by creating a trigger on your database tables you can create a log record with timestamp values. Then you can query your log table to determine which records are inserted, updated or deleted from your source tables.
For example, following is from one of my test trigger codes
CREATE TRIGGER "A00077387"."SALARY_A_UPD" AFTER UPDATE ON "A00077387"."SALARY" REFERENCING OLD ROW MYOLDROW,
NEW ROW MYNEWROW FOR EACH ROW
begin INSERT
INTO SalaryLog ( Employee,
Salary,
Operation,
DateTime ) VALUES ( :mynewrow.Employee,
:mynewrow.Salary,
'U',
CURRENT_DATE )
;
end
;
You can create AFTER INSERT and AFTER DELETE triggers as well similar to AFTER UPDATE
You can organize your Log table so that so can track more than one table if you wish just by keeping table name, PK fields and values, operation type, timestamp values, etc.
But it is better and easier to use seperate Log tables for each table.
Suppose I have a dynamo table named Person, which has 2 fields, name (string), age (int). Let's assume it has a TB worth of data and experiences a small amount of read throughput, but a ton of write throughput. Now I want to add a new field called Phone (string). What is the best way to go about moving the data from one table to another?
Note: Dynamo doesn't let you rename tables, and fields cannot be null.
Here are the options I think I have:
Dump the table to .csv, run a script (overnight probably since it's a TB worth of data) to add a default phone number to this file. (Not ideal, will also lose all new data submitted into old table, unless I bring the service offline to perform the migration (which is not an option in this case)).
Use the SCAN api call. (SCAN will read all values, then will consume significant write throughput on the new table to insert all old data into it).
How can I do perform a dynamo migration on a large table w/o
significant data loss?
you don't need to do anything. This is NoSQL, not SQL. (i.e. there is no idiomatic way to do this as you normally don't need migrations for NoSQL)
Just start writing entries with the additional key.
Records you get back that are written before will not have this key. What you normally do is have a default value you use when missing.
If you want to backfill, just go through and read the value + put the value with the additional field. You can do this in one run via a scan or again do it lazily when accessing the data.
It's my first time using a NoSQL database so I'm really confused. I'd really appreciate any help I can get.
I want to store data comprising announcements in my table. Essentially, each announcement has an ID, a date, and a text.
So for example, an announcement might have ID of 1, date of 2014/02/26, and text of "This is a sample announcement". Newer announcements always have a greater ID value than older announcements, since they are added to the table later.
There are two types of queries I want to run on this table:
I want to retrieve the text of the announcements sorted in order of date.
I want to retrieve the text and dates of the x most recent announcements (say, the 3 most recent announcements).
So I've set up the table with the following attributes:
ID (number) as primary key, and
date (string) as range
Is this appropriate for what my use cases? And if so, what kind of query/reads/requests/scans/whatever (I'm really confused about the terminology here too) should I be running to accomplish the two types of queries I want to make?
Any help will be very much appreciated. Thanks!
You are on the right track.
As far as sorting, DynamoDB will sort by the range key, so date will work but I'd recommend storing it as a number, perhaps milliseconds since the Unix epoch, rather than a String. This will make it trivial to get the announcements in ascending or descending order based on their created date.
See this answer for an overview of local vs global secondary indexes and what capabilities they provide: Optional secondary indexes in DynamoDB
As far as retrieving all items, you would need to perform a scan. Scans are not as efficient as queries, but since all of Dynamo is on SSD's they're still relatively quick. You don't get the single digit millisecond performance with a scan that you get with a query, so if there's a way to associate announcements with a user ID, you might get better performance than with a scan.
Note that you cannot modify the table schema (hash key, range key, and indexes) after you create the table. There are ways to manually migrate a table or import/export it, but the point is that you should think hard about current and future query requirements up front and design the table to support them. It's very easy to add or stop storing non-key or non-item attributes though, which provides nice flexibility.
Finally, try to avoid thinking of Dynamo as relational. With Dynamo, in a lot of cases you may well be better off de normalizing or duplicating some of the data in exchange for fast query performance.
I want to process all of the data in a column family in a MapReduce job. Ordering is not important.
An approach is to iterate over all the row keys of the column family to use as the input. This could be potentially a bottleneck and could replaced with a parallel method.
I'm open to other suggestions, or for someone to tell me I'm wasting my time with this idea. I'm currently investigating the following:
A potentially more efficient way is to assign ranges to the input instead of iterating over all row keys (before the mapper starts). Since I am using RandomPartitioner, is there a way to specify a range to query based on the MD5?
For example, I want to split the task into 16 jobs. Since the RandomPartitioner is MD5 based (from what I have read), I'd like to query everything starting with a for the first range. In other words, how would I query do a get_range on the MD5 with the start of a and ends before b. e.g. a0000000000000000000000000000000 - afffffffffffffffffffffffffffffff?
I'm using the pycassa API (Python) but I'm happy to see Java examples.
I'd cheat a little:
Create new rows job_(n) with each column representing each row key in the range you want
Pull all columns from that specific row to indicate which rows you should pull from the CF
I do this with users. Users from a particular country get a column in the country specific row. Users with a particular age are also added to a specific row.
Allows me to quickly pull the rows i need based on the criteria i want and is a little more efficient compared to pulling everything.
This is how the Mahout CassandraDataModel example functions:
https://github.com/apache/mahout/blob/trunk/integration/src/main/java/org/apache/mahout/cf/taste/impl/model/cassandra/CassandraDataModel.java
Once you have the data and can pull the rows you are interested in, you can hand it off to your MR job(s).
Alternately, if speed isn't an issue, look into using PIG: How to use Cassandra's Map Reduce with or w/o Pig?