Category: apache Presto

Discuss Apache Presto ”; Previous Next JApache Presto is an open source distributed SQL engine. Presto originated at Facebook for data analytics needs and later was open sourced. Now, Teradata joins Presto community and offers support. Apache Presto is very useful for performing queries even petabytes of data. Extensible architecture and storage plugin interfaces are very easy to interact with other file systems. Most of today’s best industrial companies are adopting Presto for its interactive speeds and low latency performance. This tutorial explores Presto architecture, configuration, and storage plugins. It discusses the basic and advanced queries and finally concludes with real-time examples. Print Page Previous Next Advertisements ”;

Apache Presto – SQL Functions ”; Previous Next As of now we were discussing running some simple basic queries on Presto. This chapter will discuss the important SQL functions. Math Functions Math functions operate on mathematical formulas. Following table describes the list of functions in detail. S.No. Function & Description 1. abs(x) Returns the absolute value of x 2. cbrt(x) Returns the cube root of x 3. ceiling(x) Returns the x value rounded up to the nearest integer 4. ceil(x) Alias for ceiling(x) 5. degrees(x) Returns the degree value for x 6. e(x) Returns the double value for Euler’s number 7. exp(x) Returns the exponent value for Euler’s number 8. floor(x) Returns x rounded down to the nearest integer 9. from_base(string,radix) Returns the value of string interpreted as a base-radix number 10. ln(x) Returns the natural logarithm of x 11. log2(x) Returns the base 2 logarithm of x 12. log10(x) Returns the base 10 logarithm of x 13. log(x,y) Returns the base y logarithm of x 14. mod(n,m) Returns the modulus (remainder) of n divided by m 15. pi() Returns pi value. The result will be returned as a double value 16. power(x,p) Returns power of value ‘p’ to the x value 17. pow(x,p) Alias for power(x,p) 18. radians(x) converts the angle x in degree radians 19. rand() Alias for radians() 20. random() Returns the pseudo-random value 21. rand(n) Alias for random() 22. round(x) Returns the rounded value for x 23. round(x,d) x value rounded for the ‘d’ decimal places 24. sign(x) Returns the signum function of x, i.e., 0 if the argument is 0 1 if the argument is greater than 0 -1 if the argument is less than 0 For double arguments, the function additionally returns − NaN if the argument is NaN 1 if the argument is +Infinity -1 if the argument is -Infinity 25. sqrt(x) Returns the square root of x 26. to_base(x,radix) Return type is archer. The result is returned as the base radix for x 27. truncate(x) Truncates the value for x 28. width_bucket(x, bound1, bound2, n) Returns the bin number of x specified bound1 and bound2 bounds and n number of buckets 29. width_bucket(x, bins) Returns the bin number of x according to the bins specified by the array bins Trigonometric Functions Trigonometric functions arguments are represented as radians(). Following table lists out the functions. S.No Functions & Description 1. acos(x) Returns the inverse cosine value(x) 2. asin(x) Returns the inverse sine value(x) 3. atan(x) Returns the inverse tangent value(x) 4. atan2(y,x) Returns the inverse tangent value(y/x) 5. cos(x) Returns the cosine value(x) 6. cosh(x) Returns the hyperbolic cosine value(x) 7. sin(x) Returns the sine value(x) 8. tan(x) Returns the tangent value(x) 9. tanh(x) Returns the hyperbolic tangent value(x) Bitwise Functions The following table lists out the Bitwise functions. S.No Functions & Description 1. bit_count(x, bits) Count the number of bits 2. bitwise_and(x,y) Perform bitwise AND operation for two bits, x and y 3. bitwise_or(x,y) Bitwise OR operation between two bits x, y 4. bitwise_not(x) Bitwise Not operation for bit x 5. bitwise_xor(x,y) XOR operation for bits x, y String Functions Following table lists out the String functions. S.No Functions & Description 1. concat(string1, …, stringN) Concatenate the given strings 2. length(string) Returns the length of the given string 3. lower(string) Returns the lowercase format for the string 4. upper(string) Returns the uppercase format for the given string 5. lpad(string, size, padstring) Left padding for the given string 6. ltrim(string) Removes the leading whitespace from the string 7. replace(string, search, replace) Replaces the string value 8. reverse(string) Reverses the operation performed for the string 9. rpad(string, size, padstring) Right padding for the given string 10. rtrim(string) Removes the trailing whitespace from the string 11. split(string, delimiter) Splits the string on delimiter and returns an array of size at the most limit 12. split_part(string, delimiter, index) Splits the string on delimiter and returns the field index 13. strpos(string, substring) Returns the starting position of the substring in the string 14. substr(string, start) Returns the substring for the given string 15. substr(string, start, length) Returns the substring for the given string with the specific length 16. trim(string) Removes the leading and trailing whitespace from the string Date and Time Functions Following table lists out the Date and Time functions. S.No Functions & Description 1. current_date Returns the current date 2. current_time Returns the current time 3. current_timestamp Returns the current timestamp 4. current_timezone() Returns the current timezone 5. now() Returns the current date,timestamp with the timezone 6. localtime Returns the local time 7. localtimestamp Returns the local timestamp Regular Expression Functions The following table lists out the Regular Expression functions. S.No Functions & Description 1. regexp_extract_all(string, pattern) Returns the string matched by the regular expression for the pattern 2. regexp_extract_all(string, pattern, group) Returns the string matched by the regular expression for the pattern and the group 3. regexp_extract(string, pattern) Returns the first substring matched by the regular expression for the pattern 4. regexp_extract(string, pattern, group) Returns the first substring matched by the regular expression for the pattern and the group 5. regexp_like(string, pattern) Returns the string matches for the pattern. If the string is returned, the value will be true otherwise false 6. regexp_replace(string, pattern) Replaces the instance of the string matched for the expression with the pattern 7. regexp_replace(string, pattern, replacement) Replace the instance of the string matched for the expression with the pattern and replacement 8. regexp_split(string, pattern) Splits the regular expression for the given pattern JSON Functions The following table lists out JSON functions. S.No Functions & Description 1. json_array_contains(json, value) Check the value exists in a json array. If the value exists it will return true, otherwise false 2. json_array_get(json_array, index) Get the element for index in json array 3. json_array_length(json) Returns the length in json array 4. json_format(json) Returns the json structure format 5. json_parse(string) Parses the string as a json 6. json_size(json, json_path) Returns the size of the value URL Functions The following table lists

Apache Presto – HIVE Connector ”; Previous Next The Hive connector allows querying data stored in a Hive data warehouse. Prerequisites Hadoop Hive Hopefully you have installed Hadoop and Hive on your machine. Start all the services one by one in the new terminal. Then, start hive metastore using the following command, hive –service metastore Presto uses Hive metastore service to get the hive table’s details. Configuration Settings Create a file “hive.properties” under “etc/catalog” directory. Use the following command. $ cd etc $ cd catalog $ vi hive.properties connector.name = hive-cdh4 hive.metastore.uri = thrift://localhost:9083 After making all the changes, save the file and quit the terminal. Create Database Create a database in Hive using the following query − Query hive> CREATE SCHEMA tutorials; After the database is created, you can verify it using the “show databases” command. Create Table Create Table is a statement used to create a table in Hive. For example, use the following query. hive> create table author(auth_id int, auth_name varchar(50), topic varchar(100) STORED AS SEQUENCEFILE; Insert Table Following query is used to insert records in hive’s table. hive> insert into table author values (1,’ Doug Cutting’,Hadoop), (2,’ James Gosling’,java),(3,’ Dennis Ritchie’,C); Start Presto CLI You can start Presto CLI to connect Hive storage plugin using the following command. $ ./presto –server localhost:8080 –catalog hive —schema tutorials; You will receive the following response. presto:tutorials > List Schemas To list out all the schemas in Hive connector, type the following command. Query presto:tutorials > show schemas from hive; Result default tutorials List Tables To list out all the tables in “tutorials” schema, use the following query. Query presto:tutorials > show tables from hive.tutorials; Result author Fetch Table Following query is used to fetch all the records from hive’s table. Query presto:tutorials > select * from hive.tutorials.author; Result auth_id | auth_name | topic ———+—————-+——– 1 | Doug Cutting | Hadoop 2 | James Gosling | java 3 | Dennis Ritchie | C Print Page Previous Next Advertisements ”;

Apache Presto – Custom Function Application ”; Previous Next Create a Maven project to develop Presto custom function. SimpleFunctionsFactory.java Create SimpleFunctionsFactory class to implement FunctionFactory interface. package com.tutorialspoint.simple.functions; import com.facebook.presto.metadata.FunctionFactory; import com.facebook.presto.metadata.FunctionListBuilder; import com.facebook.presto.metadata.SqlFunction; import com.facebook.presto.spi.type.TypeManager; import java.util.List; public class SimpleFunctionFactory implements FunctionFactory { private final TypeManager typeManager; public SimpleFunctionFactory(TypeManager typeManager) { this.typeManager = typeManager; } @Override public List<SqlFunction> listFunctions() { return new FunctionListBuilder(typeManager) .scalar(SimpleFunctions.class) .getFunctions(); } } SimpleFunctionsPlugin.java Create a SimpleFunctionsPlugin class to implement Plugin interface. package com.tutorialspoint.simple.functions; import com.facebook.presto.metadata.FunctionFactory; import com.facebook.presto.spi.Plugin; import com.facebook.presto.spi.type.TypeManager; import com.google.common.collect.ImmutableList; import javax.inject.Inject; import java.util.List; import static java.util.Objects.requireNonNull; public class SimpleFunctionsPlugin implements Plugin { private TypeManager typeManager; @Inject public void setTypeManager(TypeManager typeManager) { this.typeManager = requireNonNull(typeManager, “typeManager is null”); //Inject TypeManager class here } @Override public <T> List<T> getServices(Class<T> type){ if (type == FunctionFactory.class) { return ImmutableList.of(type.cast(new SimpleFunctionFactory(typeManager))); } return ImmutableList.of(); } } Add Resource File Create a resource file which is specified in the implementation package. (com.tutorialspoint.simple.functions.SimpleFunctionsPlugin) Now move to the resource file location @ /path/to/resource/ Then add the changes, com.facebook.presto.spi.Plugin pom.xml Add the following dependencies to pom.xml file. <?xml version = “1.0”?> <project xmlns = “http://maven.apache.org/POM/4.0.0″ xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” xsi:schemaLocation = “http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd”> <modelVersion>4.0.0</modelVersion> <groupId>com.tutorialspoint.simple.functions</groupId> <artifactId>presto-simple-functions</artifactId> <packaging>jar</packaging> <version>1.0</version> <name>presto-simple-functions</name> <description>Simple test functions for Presto</description> <properties> <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding> </properties> <dependencies> <dependency> <groupId>com.facebook.presto</groupId> <artifactId>presto-spi</artifactId> <version>0.149</version> </dependency> <dependency> <groupId>com.facebook.presto</groupId> <artifactId>presto-main</artifactId> <version>0.149</version> </dependency> <dependency> <groupId>javax.inject</groupId> <artifactId>javax.inject</artifactId> <version>1</version> </dependency> <dependency> <groupId>com.google.guava</groupId> <artifactId>guava</artifactId> <version>19.0</version> </dependency> </dependencies> <build> <finalName>presto-simple-functions</finalName> <plugins> <!– Make this jar executable –> <plugin> <groupId>org.apache.maven.plugins</groupId> <artifactId>maven-jar-plugin</artifactId> <version>2.3.2</version> </plugin> </plugins> </build> </project> SimpleFunctions.java Create SimpleFunctions class using Presto attributes. package com.tutorialspoint.simple.functions; import com.facebook.presto.operator.Description; import com.facebook.presto.operator.scalar.ScalarFunction; import com.facebook.presto.operator.scalar.StringFunctions; import com.facebook.presto.spi.type.StandardTypes; import com.facebook.presto.type.LiteralParameters; import com.facebook.presto.type.SqlType; public final class SimpleFunctions { private SimpleFunctions() { } @Description(“Returns summation of two numbers”) @ScalarFunction(“mysum”) //function name @SqlType(StandardTypes.BIGINT) public static long sum(@SqlType(StandardTypes.BIGINT) long num1, @SqlType(StandardTypes.BIGINT) long num2) { return num1 + num2; } } After the application is created compile and execute the application. It will produce the JAR file. Copy the file and move the JAR file into the target Presto server plugin directory. Compilation mvn compile Execution mvn package Now restart Presto server and connect Presto client. Then execute the custom function application as explained below, $ ./presto –catalog mysql –schema default Query presto:default> select mysum(10,10); Result _col0 ——- 20 Print Page Previous Next Advertisements ”;

Apache Presto – Quick Guide ”; Previous Next Apache Presto – Overview Data analytics is the process of analyzing raw data to gather relevant information for better decision making. It is primarily used in many organizations to make business decisions. Well, big data analytics involves a large amount of data and this process is quite complex, hence companies use different strategies. For example, Facebook is one of the leading data driven and largest data warehouse company in the world. Facebook warehouse data is stored in Hadoop for large scale computation. Later, when warehouse data grew to petabytes, they decided to develop a new system with low latency. In the year of 2012, Facebook team members designed “Presto” for interactive query analytics that would operate quickly even with petabytes of data. What is Apache Presto? Apache Presto is a distributed parallel query execution engine, optimized for low latency and interactive query analysis. Presto runs queries easily and scales without down time even from gigabytes to petabytes. A single Presto query can process data from multiple sources like HDFS, MySQL, Cassandra, Hive and many more data sources. Presto is built in Java and easy to integrate with other data infrastructure components. Presto is powerful, and leading companies like Airbnb, DropBox, Groupon, Netflix are adopting it. Presto − Features Presto contains the following features − Simple and extensible architecture. Pluggable connectors – Presto supports pluggable connector to provide metadata and data for queries. Pipelined executions – Avoids unnecessary I/O latency overhead. User-defined functions – Analysts can create custom user-defined functions to migrate easily. Vectorized columnar processing. Presto − Benefits Here is a list of benefits that Apache Presto offers − Specialized SQL operations Easy to install and debug Simple storage abstraction Quickly scales petabytes data with low latency Presto − Applications Presto supports most of today’s best industrial applications. Let’s take a look at some of the notable applications. Facebook − Facebook built Presto for data analytics needs. Presto easily scales large velocity of data. Teradata − Teradata provides end-to-end solutions in Big Data analytics and data warehousing. Teradata contribution to Presto makes it easier for more companies to enable all analytical needs. Airbnb − Presto is an integral part of the Airbnb data infrastructure. Well, hundreds of employees are running queries each day with the technology. Why Presto? Presto supports standard ANSI SQL which has made it very easy for data analysts and developers. Though it is built in Java, it avoids typical issues of Java code related to memory allocation and garbage collection. Presto has a connector architecture that is Hadoop friendly. It allows to easily plug in file systems. Presto runs on multiple Hadoop distributions. In addition, Presto can reach out from a Hadoop platform to query Cassandra, relational databases, or other data stores. This cross-platform analytic capability allows Presto users to extract maximum business value from gigabytes to petabytes of data. Apache Presto – Architecture The architecture of Presto is almost similar to classic MPP (massively parallel processing) DBMS architecture. The following diagram illustrates the architecture of Presto. The above diagram consists of different components. Following table describes each of the component in detail. S.No Component & Description 1. Client Client (Presto CLI) submits SQL statements to a coordinator to get the result. 2. Coordinator Coordinator is a master daemon. The coordinator initially parses the SQL queries then analyzes and plans for the query execution. Scheduler performs pipeline execution, assigns work to the closest node and monitors progress. 3. Connector Storage plugins are called as connectors. Hive, HBase, MySQL, Cassandra and many more act as a connector; otherwise you can also implement a custom one. The connector provides metadata and data for queries. The coordinator uses the connector to get metadata for building a query plan. 4. Worker The coordinator assigns task to worker nodes. The workers get actual data from the connector. Finally, the worker node delivers result to the client. Presto − Workflow Presto is a distributed system that runs on a cluster of nodes. Presto’s distributed query engine is optimized for interactive analysis and supports standard ANSI SQL, including complex queries, aggregations, joins, and window functions. Presto architecture is simple and extensible. Presto client (CLI) submits SQL statements to a master daemon coordinator. The scheduler connects through execution pipeline. The scheduler assigns work to nodes which is closest to the data and monitors progress. The coordinator assigns task to multiple worker nodes and finally the worker node delivers the result back to the client. The client pulls data from the output process. Extensibility is the key design. Pluggable connectors like Hive, HBase, MySQL, etc., provides metadata and data for queries. Presto was designed with a “simple storage abstraction” that makes it easy to provide SQL query capability against these different kind of data sources. Execution Model Presto supports custom query and execution engine with operators designed to support SQL semantics. In addition to improved scheduling, all processing is in memory and pipelined across the network between different stages. This avoids unnecessary I/O latency overhead. Apache Presto – Installation This chapter will explain how to install Presto on your machine. Let’s go through the basic requirements of Presto, Linux or Mac OS Java version 8 Now, let’s continue the following steps to install Presto on your machine. Verifying Java installation Hopefully, you have already installed Java version 8 on your machine right now, so you just verify it using the following command. $ java -version If Java is successfully installed on your machine, you could see the version of installed Java. If Java is not installed, follow the subsequent steps to install Java 8 on your machine. Download JDK. Download the latest version of JDK by visiting the following link. http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html The latest version is JDK 8u 92 and the file is “jdk-8u92-linux-x64.tar.gz”. Please download the file on your machine. After that, extract the files and move to the specific directory. Then set Java alternatives. Finally Java will be installed on your machine.

Apache Presto – JMX Connector ”; Previous Next Java Management Extensions (JMX) gives information about the Java Virtual Machine and software running inside JVM. The JMX connector is used to query JMX information in Presto server. As we have already enabled “jmx.properties” file under “etc/catalog” directory. Now connect Prest CLI to enable JMX plugin. Presto CLI Query $ ./presto –server localhost:8080 –catalog jmx –schema jmx Result You will receive the following response. presto:jmx> JMX Schema To list out all the schemas in “jmx”, type the following query. Query presto:jmx> show schemas from jmx; Result Schema ——————– information_schema current Show Tables To view the tables in the “current” schema, use the following command. Query 1 presto:jmx> show tables from jmx.current; Result Table —————————————————————————— com.facebook.presto.execution.scheduler:name = nodescheduler com.facebook.presto.execution:name = queryexecution com.facebook.presto.execution:name = querymanager com.facebook.presto.execution:name = remotetaskfactory com.facebook.presto.execution:name = taskexecutor com.facebook.presto.execution:name = taskmanager com.facebook.presto.execution:type = queryqueue,name = global,expansion = global ……………… ………………. Query 2 presto:jmx> select * from jmx.current.”java.lang:type = compilation”; Result node | compilationtimemonitoringsupported | name | objectname | totalcompilationti ————————————–+————————————+——————————–+—————————-+——————- ffffffff-ffff-ffff-ffff-ffffffffffff | true | HotSpot 64-Bit Tiered Compilers | java.lang:type=Compilation | 1276 Query 3 presto:jmx> select * from jmx.current.”com.facebook.presto.server:name = taskresource”; Result node | readfromoutputbuffertime.alltime.count | readfromoutputbuffertime.alltime.max | readfromoutputbuffertime.alltime.maxer ————————————–+—————————————+————————————–+————————————— ffffffff-ffff-ffff-ffff-ffffffffffff | 92.0 | 1.009106149 | Print Page Previous Next Advertisements ”;

Apache Presto – Architecture ”; Previous Next The architecture of Presto is almost similar to classic MPP (massively parallel processing) DBMS architecture. The following diagram illustrates the architecture of Presto. The above diagram consists of different components. Following table describes each of the component in detail. S.No Component & Description 1. Client Client (Presto CLI) submits SQL statements to a coordinator to get the result. 2. Coordinator Coordinator is a master daemon. The coordinator initially parses the SQL queries then analyzes and plans for the query execution. Scheduler performs pipeline execution, assigns work to the closest node and monitors progress. 3. Connector Storage plugins are called as connectors. Hive, HBase, MySQL, Cassandra and many more act as a connector; otherwise you can also implement a custom one. The connector provides metadata and data for queries. The coordinator uses the connector to get metadata for building a query plan. 4. Worker The coordinator assigns task to worker nodes. The workers get actual data from the connector. Finally, the worker node delivers result to the client. Presto − Workflow Presto is a distributed system that runs on a cluster of nodes. Presto’s distributed query engine is optimized for interactive analysis and supports standard ANSI SQL, including complex queries, aggregations, joins, and window functions. Presto architecture is simple and extensible. Presto client (CLI) submits SQL statements to a master daemon coordinator. The scheduler connects through execution pipeline. The scheduler assigns work to nodes which is closest to the data and monitors progress. The coordinator assigns task to multiple worker nodes and finally the worker node delivers the result back to the client. The client pulls data from the output process. Extensibility is the key design. Pluggable connectors like Hive, HBase, MySQL, etc., provides metadata and data for queries. Presto was designed with a “simple storage abstraction” that makes it easy to provide SQL query capability against these different kind of data sources. Execution Model Presto supports custom query and execution engine with operators designed to support SQL semantics. In addition to improved scheduling, all processing is in memory and pipelined across the network between different stages. This avoids unnecessary I/O latency overhead. Print Page Previous Next Advertisements ”;

Apache Presto – JDBC Interface ”; Previous Next Presto’s JDBC interface is used to access Java application. Prerequisites Install presto-jdbc-0.150.jar You can download the JDBC jar file by visiting the following link, https://repo1.maven.org/maven2/com/facebook/presto/presto-jdbc/0.150/ After the jar file has been downloaded, add it to the class path of your Java application. Create a Simple Application Let’s create a simple java application using JDBC interface. Coding − PrestoJdbcSample.java import java.sql.*; import com.facebook.presto.jdbc.PrestoDriver; //import presto jdbc driver packages here. public class PrestoJdbcSample { public static void main(String[] args) { Connection connection = null; Statement statement = null; try { Class.forName(“com.facebook.presto.jdbc.PrestoDriver”); connection = DriverManager.getConnection( “jdbc:presto://localhost:8080/mysql/tutorials”, “tutorials”, “”); //connect mysql server tutorials database here statement = connection.createStatement(); String sql; sql = “select auth_id, auth_name from mysql.tutorials.author”; //select mysql table author table two columns ResultSet resultSet = statement.executeQuery(sql); while(resultSet.next()){ int id = resultSet.getInt(“auth_id”); String name = resultSet.getString(“auth_name”); System.out.print(“ID: ” + id + “;nName: ” + name + “n”); } resultSet.close(); statement.close(); connection.close(); }catch(SQLException sqlException){ sqlException.printStackTrace(); }catch(Exception exception){ exception.printStackTrace(); } } } Save the file and quit the application. Now, start Presto server in one terminal and open a new terminal to compile and execute the result. Following are the steps − Compilation ~/Workspace/presto/presto-jdbc $ javac -cp presto-jdbc-0.149.jar PrestoJdbcSample.java Execution ~/Workspace/presto/presto-jdbc $ java -cp .:presto-jdbc-0.149.jar PrestoJdbcSample Output INFO: Logging initialized @146ms ID: 1; Name: Doug Cutting ID: 2; Name: James Gosling ID: 3; Name: Dennis Ritchie Print Page Previous Next Advertisements ”;

Apache Presto – Installation ”; Previous Next This chapter will explain how to install Presto on your machine. Let’s go through the basic requirements of Presto, Linux or Mac OS Java version 8 Now, let’s continue the following steps to install Presto on your machine. Verifying Java installation Hopefully, you have already installed Java version 8 on your machine right now, so you just verify it using the following command. $ java -version If Java is successfully installed on your machine, you could see the version of installed Java. If Java is not installed, follow the subsequent steps to install Java 8 on your machine. Download JDK. Download the latest version of JDK by visiting the following link. http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html The latest version is JDK 8u 92 and the file is “jdk-8u92-linux-x64.tar.gz”. Please download the file on your machine. After that, extract the files and move to the specific directory. Then set Java alternatives. Finally Java will be installed on your machine. Apache Presto Installation Download the latest version of Presto by visiting the following link, https://repo1.maven.org/maven2/com/facebook/presto/presto-server/0.149/ Now the latest version of “presto-server-0.149.tar.gz” will be downloaded on your machine. Extract tar Files Extract the tar file using the following command − $ tar -zxf presto-server-0.149.tar.gz $ cd presto-server-0.149 Configuration Settings Create “data” directory Create a data directory outside the installation directory, which will be used for storing logs, metadata, etc., so that it is to be easily preserved when upgrading Presto. It is defined using the following code − $ cd $ mkdir data To view the path where it is located, use the command “pwd”. This location will be assigned in the next node.properties file. Create “etc” directory Create an etc directory inside Presto installation directory using the following code − $ cd presto-server-0.149 $ mkdir etc This directory will hold configuration files. Let’s create each file one by one. Node Properties Presto node properties file contains environmental configuration specific to each node. It is created inside etc directory (etc/node.properties) using the following code − $ cd etc $ vi node.properties node.environment = production node.id = ffffffff-ffff-ffff-ffff-ffffffffffff node.data-dir = /Users/../workspace/Presto After making all the changes, save the file, and quit the terminal. Here node.data is the location path of the above created data directory. node.id represents the unique identifier for each node. JVM Config Create a file “jvm.config” inside etc directory (etc/jvm.config). This file contains a list of command line options used for launching the Java Virtual Machine. $ cd etc $ vi jvm.config -server -Xmx16G -XX:+UseG1GC -XX:G1HeapRegionSize = 32M -XX:+UseGCOverheadLimit -XX:+ExplicitGCInvokesConcurrent -XX:+HeapDumpOnOutOfMemoryError -XX:OnOutOfMemoryError = kill -9 %p After making all the changes, save the file, and quit the terminal. Config Properties Create a file “config.properties” inside etc directory(etc/config.properties). This file contains the configuration of Presto server. If you are setting up a single machine for testing, Presto server can function only as the coordination process as defined using the following code − $ cd etc $ vi config.properties coordinator = true node-scheduler.include-coordinator = true http-server.http.port = 8080 query.max-memory = 5GB query.max-memory-per-node = 1GB discovery-server.enabled = true discovery.uri = http://localhost:8080 Here, coordinator − master node. node-scheduler.include-coordinator − Allows scheduling work on the coordinator. http-server.http.port − Specifies the port for the HTTP server. query.max-memory=5GB − The maximum amount of distributed memory. query.max-memory-per-node=1GB − The maximum amount of memory per node. discovery-server.enabled − Presto uses the Discovery service to find all the nodes in the cluster. discovery.uri − he URI to the Discovery server. If you are setting up multiple machine Presto server, Presto will function as both coordination and worker process. Use this configuration setting to test Presto server on multiple machines. Configuration for Coordinator $ cd etc $ vi config.properties coordinator = true node-scheduler.include-coordinator = false http-server.http.port = 8080 query.max-memory = 50GB query.max-memory-per-node = 1GB discovery-server.enabled = true discovery.uri = http://localhost:8080 Configuration for Worker $ cd etc $ vi config.properties coordinator = false http-server.http.port = 8080 query.max-memory = 50GB query.max-memory-per-node = 1GB discovery.uri = http://localhost:8080 Log Properties Create a file “log.properties” inside etc directory(etc/log.properties). This file contains minimum log level for named logger hierarchies. It is defined using the following code − $ cd etc $ vi log.properties com.facebook.presto = INFO Save the file and quit the terminal. Here, four log levels are used such as DEBUG, INFO, WARN and ERROR. Default log level is INFO. Catalog Properties Create a directory “catalog” inside etc directory(etc/catalog). This will be used for mounting data. For example, create etc/catalog/jmx.properties with the following contents to mount the jmx connector as the jmx catalog − $ cd etc $ mkdir catalog $ cd catalog $ vi jmx.properties connector.name = jmx Start Presto Presto can be started using the following command, $ bin/launcher start Then you will see the response similar to this, Started as 840 Run Presto To launch Presto server, use the following command − $ bin/launcher run After successfully launching Presto server, you can find log files in “var/log” directory. launcher.log − This log is created by the launcher and is connected to the stdout and stderr streams of the server. server.log − This is the main log file used by Presto. http-request.log − HTTP request received by the server. As of now, you have successfully installed Presto configuration settings on your machine. Let’s continue the steps to install Presto CLI. Install Presto CLI The Presto CLI provides a terminal-based interactive shell for running queries. Download the Presto CLI by visiting the following link, https://repo1.maven.org/maven2/com/facebook/presto/presto-cli/0.149/ Now “presto-cli-0.149-executable.jar” will be installed on your machine. Run CLI After downloading the presto-cli, copy it to the location which you want to run it from. This location may be any node that has network access to the coordinator. First change the name of the Jar file to Presto. Then make it executable with chmod &plus; x command using the following code − $ mv presto-cli-0.149-executable.jar presto $ chmod +x presto Now execute CLI using the following command, ./presto –server localhost:8080 –catalog jmx –schema default Here jmx(Java Management Extension) refers to catalog and default

Apache Presto – Basic SQL Operations ”; Previous Next In this chapter, we will discuss how to create and execute queries on Presto. Let us go through Presto supported basic data types. Basic Data Types The following table describes the basic data types of Presto. S.No Data type & Description 1. VARCHAR Variable length character data 2. BIGINT A 64-bit signed integer 3. DOUBLE A 64-bit floating point double precision value 4. DECIMAL A fixed precision decimal number. For example DECIMAL(10,3) – 10 is precision, i.e. total number of digits and 3 is scale value represented as fractional point. Scale is optional and default value is 0 5. BOOLEAN Boolean values true and false 6. VARBINARY Variable length binary data 7. JSON JSON data 8. DATE Date data type represented as year-month-day 9. TIME, TIMESTAMP, TIMESTAMP with TIME ZONE TIME – Time of the day (hour-min-sec-millisecond) TIMESTAMP – Date and time of the day TIMESTAMP with TIME ZONE – Date and time of the day with time zone from the value 10. INTERVAL Stretch or extend date and time data types 11. ARRAY Array of the given component type. For example, ARRAY[5,7] 12. MAP Map between the given component types. For example, MAP(ARRAY[‘one’,’two’],ARRAY[5,7]) 13. ROW Row structure made up of named fields Presto − Operators Presto operators are listed in the following table. S.No Operator & Description 1. Arithmetic operator Presto supports arithmetic operators such as &plus;, -, &ast;, /, % 2. Relational operator <,>,<=,>=,=,<> 3. Logical operator AND, OR, NOT 4. Range operator Range operator is used to test the value in a specific range. Presto supports BETWEEN, IS NULL, IS NOT NULL, GREATEST and LEAST 5. Decimal operator Binary arithmetic decimal operator performs binary arithmetic operation for decimal type Unary decimal operator − The – operator performs negation 6. String operator The ‘||’ operator performs string concatenation 7. Date and time operator Performs arithmetic addition and subtraction operations on date and time data types 8. Array operator Subscript operator[] – access an element of an array Concatenation operator || – concatenate an array with an array or an element of the same type 9. Map operator Map subscript operator [] – retrieve the value corresponding to a given key from a map Print Page Previous Next Advertisements ”;