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ELT Processing With Couchbase and N1QL

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ELT Processing With Couchbase and N1QL

Learn how to employ an extract-load-transform style data migration process using Couchbase's N1QL for data manipulation.

· Database Zone
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Moving data between data sources is one of the key activities in data integration projects. Traditionally, techniques around data movement have been part of Data Warehouse, BI, and analytics. More recently, Big Data, Data Lakes, and Hadoop, have been frequent players in this area.

In this entry, we'll discuss how Couchbase's N1QL language can be used to make massive manipulation on the data in this kind of scenarios.

First, let us remember the two classical approaches when doing data movement:

ETL (Extract-Transform-Load): With this model, the data is extracted (from the original data source), transformed (data is reformatted to fit in the target system), and loaded (in the target data store).

ELT (Extract- Load-Transform): With this model, the data is extracted (from the original data source), then loaded in the same format in the target system. Then, we do a transformation in the target system to obtain the desired data format.

We will focus on an ELT exercise in this example. Let's do a simple export from a relational database and load the data into Couchbase. We will use Oracle Database as our input data source, with the classical HR schema example built in, which models a Human Resources department.

This is the source data model:

In the first step, we will load the data with the same structure. There is a free tool you can use to perform this initial migration here. At the end, we will have JSON documents mapping this table model:


For example, a location document will look like this:

{
    "street_address": "2017 Shinjuku-ku",
    "city": "Tokyo",
    "state_province": "Tokyo Prefecture",
    "postal_code": "1689",
    "type": "locations",
    "location_id": 1200,
    "country_id": "JP"
}


This was an easy first step. However, this mapping table-to-document is often a sign of bad design in the NoSQL world. In NoSQL, it's common to de-normalize your data in favor of a more direct access path, embedding referenced data. The goal is to minimize database interactions and joins, looking for the best performance.

Let's assume that our use case is driven by a frequent access to the whole job history for employees. We decide to change our design to this one:

For locations, we are joining in a single location document the referenced data for both country and region.

For the employee document, we will embed the department data and include an array with the whole job history for each employee. This array support in JSON is a good improvement over foreign key references and joins in the relational world.

For the job document, we will maintain the original table structure.

Now that we have extracted and loaded the data, we will transform into this model to finish our ELT example. How can we do this job? It's time for N1QL!

N1QL is the SQL-like language included with Couchbase for data access and manipulation. In this example, we will use two buckets: HR, which maps to the original Oracle HR schema, and HR_DNORM, which will hold our target document model.

We have already loaded our HR schema. The next step is to create a bucket named HR_DNORM. Then, we will create a primary index in this new bucket:

CREATE PRIMARY INDEX ON HR_DNORM


Now it is time to create the location documents. These documents are composed of original locations, country, and region documents:

INSERT INTO HR_DNORM (key _k, value _v) 
SELECT meta().id _k,
{
   "type":"location",
   "city":loc.city,
   "postal_code":loc.postal_code, 
   "state_province":IFNULL(loc.state_province, null), 
   "street_address":loc.street_address, 
   "country_name":ct.country_name, 
   "region_name":rg.region_name
 } as _v
FROM HR loc
JOIN HR ct ON KEYS "countries::" || loc.country_id
JOIN HR rg ON KEYS "regions::" || TO_STRING(ct.region_id)
WHERE loc.type="locations"


A few things to note:

  • We are using the projection of a SELECT statement to make the insert. In this example, the original data comes from a different bucket.
  • JOINs are used in the original bucket to reference countries and regions
  • The IFNULL function used to set explicitly null values for the field state_province.
  • The TO_STRING function applied on a number field to reference a key.

Our original sample becomes this:

{
  "city": "Tokyo",
  "country_name": "Japan",
  "postal_code": "1689",
  "region_name": "Asia",
  "state_province": "Tokyo Prefecture",
  "street_address": "2017 Shinjuku-ku",
  "type": "location"
}


Note that we got rid of our references location_id and country_id.

Now, it is time for our employee documents. We will do it in several steps. The first one is to create the employees from the original HR bucket, including department and actual job information:

INSERT INTO HR_DNORM (key _k, value _v) 
SELECT meta().id _k,
{
   "type":"employees",
   "employee_id": emp. employee_id,
   "first_name": emp.first_name,
   "last_name": emp.last_name,
   "phone_number": emp.phone_number,
   "email": emp.email,
   "hire_date": emp.hire_date,
   "salary": emp.salary,
   "commission_pct": IFNULL(emp.commission_pct, null),
   "manager_id": IFNULL(emp.manager_id, null),
   "job_id": emp.job_id,
   "job_title": job.job_title,
   "department" : 
   {
      "name" : dpt.department_name,
      "manager_id" : dpt.manager_id,
      "department_id" : dpt.department_id
   }  
 } as _v
FROM HR emp
JOIN HR job ON KEYS "jobs::" || emp.job_id 
JOIN HR dpt ON KEYS "departments::" || TO_STRING(emp.department_id) 
WHERE emp.type="employees" RETURNING META().id;


Second, we will use a temporary construction to build the job history array:

INSERT INTO HR_DNORM (key _k, value job_history) 
SELECT "job_history::" || TO_STRING(jobh.employee_id) AS _k, 
{
"jobs" : ARRAY_AGG(
   {
      "start_date": jobh.start_date,
      "end_date": jobh.end_date,
      "job_id": jobh.job_id,
      "department_id": jobh.department_id
   } 
)
} AS job_history
FROM HR jobh
WHERE jobh.type="job_history"
GROUP BY jobh.employee_id
RETURNING META().id;


Now is easy to update our "employees" documents by adding a job_history array:

UPDATE HR_DNORM emp
SET job_history=(
   SELECT RAW jobs
   FROM HR_DNORM jobh
   USE KEYS "job_history::" || SUBSTR(meta(emp).id, 11)
)[0]
WHERE 
emp.type="employees"
RETURNING meta().id


This is how our employee document looks:

{
  "commission_pct": null,
  "department": {
    "department_id": 10,
    "manager_id": 200,
    "name": "Administration"
  },
  "email": "JWHALEN",
  "employee_id": 200,
  "first_name": "Jennifer",
  "hire_date": "2003-09-16T22:00:00Z",
  "job_history": [
    {
      "department_id": 80,
      "end_date": "2007-12-31T23:00:00Z",
      "job_id": "SA_REP",
      "start_date": "2006-12-31T23:00:00Z"
    },
    {
      "department_id": 90,
      "end_date": "2001-06-16T22:00:00Z",
      "job_id": "AD_ASST",
      "start_date": "1995-09-16T22:00:00Z"
    },
    {
      "department_id": 90,
      "end_date": "2006-12-30T23:00:00Z",
      "job_id": "AC_ACCOUNT",
      "start_date": "2002-06-30T22:00:00Z"
    }
  ],
  "job_id": "AD_ASST",
  "job_title": "Administration Assistant",
  "last_name": "Whalen",
  "manager_id": 101,
  "phone_number": "515.123.4444",
  "salary": 4400,
  "type": "employees"
}


Note the job_history array of the previous positions.

We can now delete the temporary job_history documents:

DELETE FROM HR_DNORM emp
WHERE meta().id LIKE "job_history::%"


For our last step, we insert the original jobs documents:

INSERT INTO HR_DNORM (key _k, value _v) 
SELECT meta().id _k, _v
FROM HR _v
WHERE _v.type="jobs"


We are done. This is a simple example, but it shows can powerful N1QL can be for data manipulation. Happy data migration!

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Topics:
database ,data migration ,nosql ,elt

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