# Connecting to DynamoDB with Amazon EMR Serverless
In this tutorial, you upload a subset of data from the [United States Board on Geographic Names](https://www.usgs.gov/us-board-on-geographic-names) to an Amazon S3 bucket and then use Hive or Spark on Amazon EMR Serverless to copy the data to an Amazon DynamoDB table for querying.
## Step 1: Upload data to an Amazon S3 bucket
To create an Amazon S3 bucket, follow the instructions in [Creating a bucket](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/create-bucket.html) in the *Amazon Simple Storage Service Console User Guide*. Replace references to `amzn-s3-demo-bucket` with the name of your newly created bucket. Now your EMR Serverless application is ready to run jobs.
1. Download the sample data archive `features.zip` with the following command.
```
wget https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/samples/features.zip
```
1. Extract the `features.txt` file from the archive and access the first the few lines in the file:
```
unzip features.zip
head features.txt
```
The result should appear similar to the following.
```
1535908|Big Run|Stream|WV|38.6370428|-80.8595469|794
875609|Constable Hook|Cape|NJ|40.657881|-74.0990309|7
1217998|Gooseberry Island|Island|RI|41.4534361|-71.3253284|10
26603|Boone Moore Spring|Spring|AZ|34.0895692|-111.410065|3681
1506738|Missouri Flat|Flat|WA|46.7634987|-117.0346113|2605
1181348|Minnow Run|Stream|PA|40.0820178|-79.3800349|1558
1288759|Hunting Creek|Stream|TN|36.343969|-83.8029682|1024
533060|Big Charles Bayou|Bay|LA|29.6046517|-91.9828654|0
829689|Greenwood Creek|Stream|NE|41.596086|-103.0499296|3671
541692|Button Willow Island|Island|LA|31.9579389|-93.0648847|98
```
The fields in each line here indicate a unique identifier, name, type of natural feature, state, latitude in degrees, longitude in degrees, and height in feet.
1. Upload your data to Amazon S3
```
aws s3 cp features.txt s3://amzn-s3-demo-bucket/features/
```
## Step 2: Create a Hive table
Use Apache Spark or Hive to create a new Hive table that contains the uploaded data in Amazon S3.
------
#### [ Spark ]
To create a Hive table with Spark, run the following command.
```
import org.apache.spark.sql.SparkSession
val sparkSession = SparkSession.builder().enableHiveSupport().getOrCreate()
sparkSession.sql("CREATE TABLE hive_features \
(feature_id BIGINT, \
feature_name STRING, \
feature_class STRING, \
state_alpha STRING, \
prim_lat_dec DOUBLE, \
prim_long_dec DOUBLE, \
elev_in_ft BIGINT) \
ROW FORMAT DELIMITED \
FIELDS TERMINATED BY '|' \
LINES TERMINATED BY '\n' \
LOCATION 's3://amzn-s3-demo-bucket/features';")
```
You now have a populated Hive table with data from the `features.txt` file. To verify that your data is in the table, run a Spark SQL query as shown in the following example.
```
sparkSession.sql(
"SELECT state_alpha, COUNT(*) FROM hive_features GROUP BY state_alpha;")
```
------
#### [ Hive ]
To create a Hive table with Hive, run the following command.
```
CREATE TABLE hive_features
(feature_id BIGINT,
feature_name STRING ,
feature_class STRING ,
state_alpha STRING,
prim_lat_dec DOUBLE ,
prim_long_dec DOUBLE ,
elev_in_ft BIGINT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '|'
LINES TERMINATED BY '\n'
LOCATION 's3://amzn-s3-demo-bucket/features';
```
You now have a Hive table that contains data from the `features.txt` file. To verify that your data is in the table, run a HiveQL query, as shown in the following example.
```
SELECT state_alpha, COUNT(*) FROM hive_features GROUP BY state_alpha;
```
------
## Step 3: Copy data to DynamoDB
Use Spark or Hive to copy data to a new DynamoDB table.
------
#### [ Spark ]
To copy data from the Hive table that you created in the previous step to DynamoDB, follow **Steps 1-3** in [Copy data to DynamoDB](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/EMRforDynamoDB.Tutorial.CopyDataToDDB.html). This creates a new DynamoDB table called `Features`. You can then read data directly from the text file and copy it to your DynamoDB table, as the following example shows.
```
import com.amazonaws.services.dynamodbv2.model.AttributeValue
import org.apache.hadoop.dynamodb.DynamoDBItemWritable
import org.apache.hadoop.dynamodb.read.DynamoDBInputFormat
import org.apache.hadoop.io.Text
import org.apache.hadoop.mapred.JobConf
import org.apache.spark.SparkContext
import scala.collection.JavaConverters._
object EmrServerlessDynamoDbTest {
def main(args: Array[String]): Unit = {
jobConf.set("dynamodb.input.tableName", "Features")
jobConf.set("dynamodb.output.tableName", "Features")
jobConf.set("dynamodb.region", "region")
jobConf.set("mapred.output.format.class", "org.apache.hadoop.dynamodb.write.DynamoDBOutputFormat")
jobConf.set("mapred.input.format.class", "org.apache.hadoop.dynamodb.read.DynamoDBInputFormat")
val rdd = sc.textFile("s3://amzn-s3-demo-bucket/ddb-connector/")
.map(row => {
val line = row.split("\\|")
val item = new DynamoDBItemWritable()
val elevInFt = if (line.length > 6) {
new AttributeValue().withN(line(6))
} else {
new AttributeValue().withNULL(true)
}
item.setItem(Map(
"feature_id" -> new AttributeValue().withN(line(0)),
"feature_name" -> new AttributeValue(line(1)),
"feature_class" -> new AttributeValue(line(2)),
"state_alpha" -> new AttributeValue(line(3)),
"prim_lat_dec" -> new AttributeValue().withN(line(4)),
"prim_long_dec" -> new AttributeValue().withN(line(5)),
"elev_in_ft" -> elevInFt)
.asJava)
(new Text(""), item)
})
rdd.saveAsHadoopDataset(jobConf)
}
}
```
------
#### [ Hive ]
To copy data from the Hive table that you created in the previous step to DynamoDB, follow the instructions in [Copy data to DynamoDB](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/EMRforDynamoDB.Tutorial.CopyDataToDDB.html).
------
## Step 4: Query data from DynamoDB
Use Spark or Hive to query your DynamoDB table.
------
#### [ Spark ]
To query data from the DynamoDB table that you created in the previous step, use either Spark SQL or the Spark MapReduce API.
**Example – Query your DynamoDB table with Spark SQL**
The following Spark SQL query returns a list of all the feature types in alphabetical order.
```
val dataFrame = sparkSession.sql("SELECT DISTINCT feature_class \
FROM ddb_features \
ORDER BY feature_class;")
```
The following Spark SQL query returns a list of all lakes that begin with the letter *M*.
```
val dataFrame = sparkSession.sql("SELECT feature_name, state_alpha \
FROM ddb_features \
WHERE feature_class = 'Lake' \
AND feature_name LIKE 'M%' \
ORDER BY feature_name;")
```
The following Spark SQL query returns a list of all states with at least three features that are higher than one mile.
```
val dataFrame = sparkSession.dql("SELECT state_alpha, feature_class, COUNT(*) \
FROM ddb_features \
WHERE elev_in_ft > 5280 \
GROUP by state_alpha, feature_class \
HAVING COUNT(*) >= 3 \
ORDER BY state_alpha, feature_class;")
```
**Example – Query your DynamoDB table with the Spark MapReduce API**
The following MapReduce query returns a list of all the feature types in alphabetical order.
```
val df = sc.hadoopRDD(jobConf, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])
.map(pair => (pair._1, pair._2.getItem))
.map(pair => pair._2.get("feature_class").getS)
.distinct()
.sortBy(value => value)
.toDF("feature_class")
```
The following MapReduce query returns a list of all lakes that begin with the letter *M*.
```
val df = sc.hadoopRDD(jobConf, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])
.map(pair => (pair._1, pair._2.getItem))
.filter(pair => "Lake".equals(pair._2.get("feature_class").getS))
.filter(pair => pair._2.get("feature_name").getS.startsWith("M"))
.map(pair => (pair._2.get("feature_name").getS, pair._2.get("state_alpha").getS))
.sortBy(_._1)
.toDF("feature_name", "state_alpha")
```
The following MapReduce query returns a list of all states with at least three features that are higher than one mile.
```
val df = sc.hadoopRDD(jobConf, classOf[DynamoDBInputFormat], classOf[Text], classOf[DynamoDBItemWritable])
.map(pair => pair._2.getItem)
.filter(pair => pair.get("elev_in_ft").getN != null)
.filter(pair => Integer.parseInt(pair.get("elev_in_ft").getN) > 5280)
.groupBy(pair => (pair.get("state_alpha").getS, pair.get("feature_class").getS))
.filter(pair => pair._2.size >= 3)
.map(pair => (pair._1._1, pair._1._2, pair._2.size))
.sortBy(pair => (pair._1, pair._2))
.toDF("state_alpha", "feature_class", "count")
```
------
#### [ Hive ]
To query data from the DynamoDB table that you created in the previous step, follow the instructions in [Query the data in the DynamoDB table](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/EMRforDynamoDB.Tutorial.QueryDataInDynamoDB.html).
------