Quick exploration of data using spark shell

ratatouilleData analytics has never been easier to use than in the last decade thanks to open sources projects like Hadoop, Spark and many others.

In this post we are going to use spark-shell to read a CSV file and analyze it by running sql queries on this dataset. The CSV contains the list of restaurant inspections in NYC. It is freely available on the NYC Open Data website. You can download it at: https://data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/xx67-kt59/data

Click on Export, then CSV.

Installing spark-shell

To explore the dataset, we use spark-shell

Download spark 2.0.2 at http://spark.apache.org/downloads.html

Uncompress it:

tar xvfz Downloads/spark-2.0.2-bin-hadoop2.7.tgz

Run spark-shell:

cd spark-2.0.2-bin-hadoop2.7/

To load the inspection file in a rdd:

import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder().getOrCreate()
val csvFile =  "/Users/chimpler/Downloads/DOHMH_New_York_City_Restaurant_Inspection_Results.csv"
val inspections = spark.read.option("header",true).csv(csvFile)

Now let’s have a quick look at the dataframe content:

// columns
scala> inspections.printSchema
 |-- CAMIS: string (nullable = true)
 |-- DBA: string (nullable = true)
 |-- BORO: string (nullable = true)
 |-- BUILDING: string (nullable = true)
 |-- STREET: string (nullable = true)
 |-- ZIPCODE: string (nullable = true)
 |-- PHONE: string (nullable = true)
 |-- CUISINE DESCRIPTION: string (nullable = true)
 |-- INSPECTION DATE: string (nullable = true)
 |-- ACTION: string (nullable = true)
 |-- VIOLATION CODE: string (nullable = true)
 |-- VIOLATION DESCRIPTION: string (nullable = true)
 |-- CRITICAL FLAG: string (nullable = true)
 |-- SCORE: string (nullable = true)
 |-- GRADE: string (nullable = true)
 |-- GRADE DATE: string (nullable = true)
 |-- RECORD DATE: string (nullable = true)
 |-- INSPECTION TYPE: string (nullable = true)

// size of the dataset
scala> inspections.count
res42: Long = 436999

Let’s run some SQL queries on the dataset:

// register the table so we can access it from SQL
scala> inspections.registerTempTable("inspections")
scala> val df = sql("""
    COUNT(1) AS Num
FROM inspections
scala> df.show
| American            |101265|
| Chinese             | 47391|
| Latin (Cuban, Dom...| 20778|
| Pizza               | 20763|
| Italian             | 20084|
| Mexican             | 16424|
| Café/Coffee/Tea    | 16016|
| Japanese            | 15624|
| Caribbean           | 13615|
| Bakery              | 13200|

As expected, since the inspections are taking place in New York, the top cuisines served in restaurants are American, followed by Chinese, Pizza and Italian.

Now let’s look at the distribution of the grades that the inspectors are giving to the restaurants:

scala> val df2 = sql("""
    GRADE, COUNT(1) AS Num FROM inspections

scala> df2.show()
| GRADE        | Num  |
| null         |228417|
| A            |160200|
| B            | 33794|
| C            |  8551|
| Z            |  2689|
|Not Yet Graded|  1943|
| P            |  1405|

It shows that most of the restaurants are not graded yet and that among the graded restaurants, most of them are graded A.

Now let’s look at the number of A, B, C grades for each cuisine:

scala> val df3 = sql("""
    SUM(IF(GRADE='A', 1, 0)) AS A,
    SUM(IF(GRADE='B', 1, 0)) AS B,
    SUM(IF(GRADE='C', 1, 0)) AS C
FROM inspections

scala> df3.show()
| Pancakes/Waffles  |  128|  10|   0|
| Chinese/Japanese  |  342| 106|  24|
| Mexican           | 5210|1471| 412|
| Jewish/Kosher     | 2281| 445| 134|
| Bakery            | 4937|1027| 250|
| Turkish           |  413|  86|  18|
| Scandinavian      |   49|   5|   0|

We can create a view out of the result of this query:

scala> df3.registerTempTable("grades")

Note that this view is not materialized so the query on this “grades” table will be ran on the inspections table.

From this, let’s find the cuisine that shows the worst ratio of Grade C. We filter out the Cuisines having less than 1,000 inspections to have meaningful results:

scala> val query = """
     C / (A + B + C) AS Ratio,
     A + B + C AS Num
  FROM grades
  WHERE A + B + C > 1000
scala> sql(query).show()
| CUISINE DESCRIPTION | Ratio             | Num|
| Asian               |0.08726249120337791|2842|
| Korean              |0.07679395719681074|2383|
| Spanish             |0.06973732889382168|5406|
| Latin (Cuban, Dom...| 0.0690481625199726|8762|
| Indian              |0.06473293243721259|2827|

Speeding up the queries

Running a SQL query takes some time, you can make the query faster by caching the intermediate result:

// Without caching:

scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 1454

// With caching
scala> df3.cache()
// Because the caching is lazy, the first time we run the query is slow
scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 1848

scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 446

If the data cannot be cached because it’s too huge to fit into memory, you can use another storage than CSV. Parquet and ORC are storing data in columnar format which allows the engine to scan only the data of the columns required for the query as opposed to all the data.

// unfortunately, parquet does not play well with spaces in column names, so
// we have to define aliases for those columns

CREATE TABLE inspections_parquet USING parquet AS
FROM inspections""")

Now let’s run the same query than previously but on this “inspections_parquet” table

val df4 = sql("""
    SUM(IF(GRADE='A', 1, 0)) AS A,
    SUM(IF(GRADE='B', 1, 0)) AS B,
    SUM(IF(GRADE='C', 1, 0)) AS C
FROM inspections_parquet

scala> df4.registerTempTable("grades_parquet")
scala> val query = """
     C / (A + B + C) AS Ratio,
     A + B + C AS Num
  FROM grades_parquet
  WHERE A + B + C > 1000
scala> sql(query).show()
scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 549
# with caching
scala> df4.cache()
scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 626
scala> val startTime = System.currentTimeMillis ; sql(query).collect() ; println(s"Took ${System.currentTimeMillis - startTime}")
Took 263

Without caching the query is now running in 549ms(parquet) vs 1,454ms(CSV).
With caching 263ms(parquet) vs 443ms(CSV). Not too shabby for just a storage format change.


In this post, we read a CSV file and analyze it using spark-shell. We saw some techniques to make the query faster by caching the data in memory or using a different a different storage format.

Another way to speed up the queries is to make them run on a spark cluster, it can be the subject of another post.

Alternatively to spark-shell, you can use spark-notebook and apache zeppelin that are web dashboards  that you can create in the browser to display grid and charts using Scala and SQL code.

Building a food recommendation engine with Spark / MLlib and Play

Recommendation engines have become very popular in the last decade with the explosion of e-commerce, on demand music and movie services, dating sites, local reviews, news aggregation and advertising (behavioral targeting, intent targeting, …). Depending on your past actions (e.g., purchases, reviews left, pages visited, …) or your interests (e.g., Facebook likes, Twitter follows), the recommendation engine will present other products that might interest you using other users actions and user behaviors (page clicks, page views, time spent on page, clicks on images/reviews, …).

In this post, we’re going to implement a recommender for food using Apache Spark and MLlib. So for instance, if one is interested by some coffee products then we might recommend her some other coffee brands, coffee filters or some related products that some other users like too.

Read more of this post

Segmenting Audience with KMeans and Voronoi Diagram using Spark and MLlib

Analyzing huge data set to extract meaningful properties can be a difficult task. Several methods have been developed for the last 50 years to find hidden information.

Clustering algorithms can be used to group similar news like in Google News, find areas with high crime concentration, find trends, .. and segment the data into groups. This segmentation can be used for instance by publisher to reach a specific target audience.

In this post, we will be using the k-means clustering algorithm implemented in Spark Machine Learning Library(MLLib) to segment the dataset by geolocation .


The k-mean clustering algorithm is an unsupervised algorithm meaning that you don’t need to provide a training example for it to work(unlike neural network, SVM, Naives Bayes classifiers, …). It partitions observations into clusters in which each observation belongs to the cluster with the nearest mean. The algorithm takes as input the observations, the number of clusters(denoted k) that we want to partition the observation into and the number of iterations. It gives as a result the centers of the clusters.

The algorithm works as follow:

  1. Take k random observations out of the dataset. Set the k centers of the clusters to those points
  2. For each observation, find the cluster center which is the closest and assign this observation to this cluster
  3. For each cluster, compute the new center by taking the average of the features of the observations assigned to this dataset
  4. Go back to 2 and repeat this for a given number of iterations

The centers of the clusters will converge and will minimize the cost function which is the sum of the square distance of each observation to their assigned cluster centers.

This minimum might be a local optimum and will depend on the observation that were randomly taken at the beginning of the algorithm.
Read more of this post

Implementing a real-time data pipeline with Spark Streaming

Real-time analytics has become a very popular topic in recent years. Whether it is in finance (high frequency trading), adtech (real-time bidding), social networks (real-time activity), Internet of things (sensors sending real-time data), server/traffic monitoring, providing real-time reporting can bring tremendous value (e.g., detect potential attacks on network immediately, quickly adjust ad campaigns, …). Apache Storm is one of the most popular frameworks to aggregate data in real-time but there are also many others such as Apache S4, Apache Samza, Akka Streams, SQLStream and more recently Spark Streaming.

According to Kyle Moses, on his page on Spark Streaming, it can process about 400,000 records / node / second for simple aggregations on small records and significantly outperforms other popular streaming systems such as Apache Storm (40x) and Yahoo S4 (57x). This can be mainly explained because Apache Storm processes messages individually while Apache Spark groups messages in small batches. Moreover in case of failure, where in Storm messages can be replayed multiple times, Spark Streaming batches are only processed once which greatly simplifies the logic (e.g., to make sure some values are not counted multiple times).

At a higher level, we can see Spark Streaming as a layer on top of Spark where data streams (coming from various sources such as Kafka, ZeroMQ, Twitter, …) can be transformed and batched in a sequence of RDDs (Resilient Distributed DataSets) using a sliding window. These RDDs can then be manipulated using normal Spark operations.

In this post we consider an adnetwork where adservers log impressions in Apache Kafka (distributed publish-subscribe messaging system). These impressions are then aggregated by Spark Streaming into a datawarehouse (here MongoDB to simplify). Usually the data is further aggregated into a fast data access layer (direct lookup) and accessible through an API as depicted in the figure below.

adnetwork architecture
Figure 1. Ad network architecture
Read more of this post

Classifiying documents using Naive Bayes on Apache Spark / MLlib

Apache SparkIn recent years, Apache Spark has gained in popularity as a faster alternative to Hadoop and it reached a major milestone last month by releasing the production ready version 1.0.0. It claims to be up to a 100 times faster by leveraging the distributed memory of the cluster and by not being tied to the multi stage execution of Map/Reduce. Like Hadoop, it offers a similar ecosystem with a database (Shark SQL), a machine learning library (MLlib), a graph library (GraphX) and many other tools built on top of Spark. Finally Spark integrates well with Scala and one can manipulate distributed collections just like regular Scala collections and Spark will take care of distributing the processing to the different workers.

In this post, we describe how we used Spark / MLlib to classify HTML documents using the popular Reuters 21578 collection of documents that appeared on Reuters newswire in 1987 as a training set.
Read more of this post

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