- Spark Create Table
- Ignite Create Table Java
- Apache Ignite Create Table
- Ignite Create Table If Not Exists
- Apache Ignite Create Temp Table
Ignite ignite = Ignition.start ; ignite.getOrCreateCache ( 'test' ).query ( new SqlFieldsQuery ( 'CREATE TABLE IF NOT EXISTS City (id LONG PRIMARY KEY,' + ' name VARCHAR) WITH ' template=replicated')); ignite.getOrCreateCache ( 'test' ).query ( new SqlFieldsQuery ( 'CREATE TABLE IF NOT EXISTS City (id LONG PRIMARY KEY,' + ' name VARCHAR) WITH ' template=replicated')). CREATE TABLE city ( id LONG PRIMARY KEY, name VARCHAR ) WITH 'template=replicated'; CREATE TABLE person ( id LONG, name VARCHAR, cityid LONG, PRIMARY KEY (id, cityid) ) WITH 'backups=1, affinityKey=cityid'; Then executing the following code provides table meta-information: // Using SparkBuilder provided by Ignite. You have 2 tables in MySql - the prefered way in Ignite is to load each table in separate cache. When you load data from MySql rows from database automatically transformed for Java types (POJO) and stored in caches. /. Create JDBC type for organization. 1 day ago In the table insert you can see the vendors, sorted by Net Score and the Shared N column, which represents the number of shared accounts in the data set. On both dimensions, bigger is better.
Put value in cache¶
Get value from cache¶
Get multiple values from cache¶
Type hints usage¶
As a rule of thumb:
- when a pyignite method or function deals with a single value or key, ithas an additional parameter, like value_hint or key_hint, which acceptsa parser/constructor class,
- nearly any structure element (inside dict or list) can be replaced witha two-tuple of (said element, type hint).
Refer the Data Types section for the full listof parser/constructor classes you can use as type hints.
scan() method queries allows youto get the whole contents of the cache, element by element.
Let us put some data in cache.
scan() returns a generator, that yieldstwo-tuples of key and value. You can iterate through the generated pairsin a safe manner:
Ufc 230 betting predictions. Or, alternatively, you can convert the generator to dictionary in one go:
But be cautious: if the cache contains a large set of data, the dictionarymay eat too much memory!
Destroy created cache and close connection.
These examples are similar to the ones given in the Apache Ignite SQLDocumentation: Getting Started.
First let us establish a connection.
Then create tables. Begin with Country table, than proceed with relatedtables City and CountryLanguage.
Fill tables with data.
Data samples are taken from Ignite GitHub repository.
That concludes the preparation of data. Now let us answer some questions.
What are the 10 largest cities in our data sample (population-wise)?¶
sql() method returns a generator,that yields the resulting rows.
What are the 10 most populated cities throughout the 3 chosen countries?¶
If you set the include_field_names argument to True, the
sql() method will generate a list ofcolumn names as a first yield. You can access field names with Python built-innext function.
Display all the information about a given city¶
Finally, delete the tables used in this example with the following queries:
Complex object (that is often called ‘Binary object’) is an Ignite datatype, that is designed to represent a Java class. It have the followingfeatures:
- have a unique ID (type id), which is derives from a class name (type name),
- have one or more associated schemas, that describes its inner structure (theorder, names and types of its fields). Each schema have its own ID,
- have an optional version number, that is aimed towards the end usersto help them distinguish between objects of the same type, serializedwith different schemas.
Unfortunately, these distinctive features of the Complex object have few to nomeaning outside of Java language. Python class can not be defined by its name(it is not unique), ID (object ID in Python is volatile; in CPython it is justa pointer in the interpreter’s memory heap), or complex of its fields (theydo not have an associated data types, moreover, they can be added or deletedin run-time). For the pyignite user it means that for all purposesof storing native Python data it is better to use Ignite
MapObject data types.
However, for interoperability purposes, pyignite has a mechanism of creatingspecial Python classes to read or write Complex objects. These classes havean interface, that simulates all the features of the Complex object: type name,type ID, schema, schema ID, and version number.
Assuming that one concrete class for representing one Complex object canseverely limit the user’s data manipulation capabilities, all thefunctionality said above is implemented through the metaclass:
GenericObjectMeta. This metaclass is usedautomatically when reading Complex objects.
Here you can see how
GenericObjectMeta usesattrs package internally for creating nice __init__() and __repr__()methods.
You can reuse the autogenerated class for subsequent writes:
GenericObjectMeta can also be used directlyfor creating custom classes:
Note how the Person class is defined. schema is a
GenericObjectMeta metaclass parameter.Another important
GenericObjectMeta parameteris a type_name, but it is optional and defaults to the class name (‘Person’in our example).
Note also, that Person do not have to define its own attributes, methods andproperties (pass), although it is completely possible.
Now, when your custom Person class is created, you are ready to send datato Ignite server using its objects. The client will implicitly register yourclass as soon as the first Complex object is sent. If you intend to use yourcustom class for reading existing Complex objects’ values before all, you mustregister said class explicitly with your client:
Now, when we dealt with the basics of pyignite implementation of ComplexObjects, let us move on to more elaborate examples.
Ignite SQL uses Complex objects internally to represent keys and rowsin SQL tables. Normally SQL data is accessed via queries (see SQL),so we will consider the following example solely for the demonstrationof how Binary objects (not Ignite SQL) work.
In the previous examples we have created some SQL tables.Let us do it again and examine the Ignite storage afterwards.
We can see that Ignite created a cache for each of our tables. The caches areconveniently named using ‘SQL_<schema name>_<table name>’ pattern.
Now let us examine a configuration of a cache that contains SQL datausing a
The values of value_type_name and key_type_name are names of the binarytypes. The City table’s key fields are stored using key_type_name type,and the other fields − value_type_name type.
Now when we have the cache, in which the SQL data resides, and the namesof the key and value data types, we can read the data without using SQLfunctions and verify the correctness of the result.
What we see is a tuple of key and value, extracted from the cache. Both keyand value are represent Complex objects. The dataclass names are the sameas the value_type_name and key_type_name cache settings. The objects’fields correspond to the SQL query.
Now, that we aware of the internal structure of the Ignite SQL storage,we can create a table and put data in it using only key-value functions.
For example, let us create a table to register High School students:a rough equivalent of the following SQL DDL statement:
These are the necessary steps to perform the task.
- Create table cache.
- Define Complex object data class.
- Insert row.
Now let us make sure that our cache really can be used with SQL functions.
Note, however, that the cache we create can not be dropped with DDL command.
It should be deleted as any other key-value cache.
Spark Create Table
Suppose we have an accounting app that stores its data in key-value format.Our task would be to introduce the following changes to the original expensevoucher’s format and data:
- rename date to expense_date,
- add report_date,
- set report_date to the current date if reported is True, None if False,
- delete reported.
First get the vouchers’ cache.
If you do not store the schema of the Complex object in code, you can obtainit as a dataclass property with
Let us modify the schema and create a new Complex object class with an updatedschema.
Now migrate the data from the old schema to the new one.
At this moment all the fields, defined in both of our schemas, can beavailable in the resulting binary object, depending on which schema was usedwhen writing it using
put() or similar methods.Ignite Binary API do not have the method to delete Complex object schema;all the schemas ever defined will stay in cluster until its shutdown.
This versioning mechanism is quite simple and robust, but it have itslimitations. The main thing is: you can not change the type of the existingfield. If you try, you will be greeted with the following message:
As an alternative, you can rename the field or create a new Complex object.
When connection to the server is broken or timed out,
Client object propagates an original exception(OSError or SocketError), but keeps its constructor’s parameters intactand tries to reconnect transparently.
When there’s no way for
Client to reconnect, itraises a special
The following example features a simple node list traversal failover mechanism.Gather 3 Ignite nodes on localhost into one cluster and run:
Then try shutting down and restarting nodes, and see what happens.
Client reconnection do not require an explicit user action, like callinga special method or resetting a parameter. Note, however, that reconnectionis lazy: it happens only if (and when) it is needed. In this example,the automatic reconnection happens, when the script checks upon the lastsaved value:
It means that instead of checking the connection status it is better forpyignite user to just try the supposed data operations and catchthe resulting exception.
connect() method accepts anyiterable, not just list. It means that you can implement any reconnectionpolicy (round-robin, nodes prioritization, pause on reconnect or gracefulbackoff) with a generator.
pyignite comes with a sample
RoundRobin generator. In the aboveexample try to replace
The client will try to reconnect to node 1 after node 3 is crashed, then tonode 2, et c. At least one node should be active for the
RoundRobin to work properly.
There are some special requirements for testing SSL connectivity.
The Ignite server must be configured for securing the binary protocol port.The server configuration process can be split up into these basic steps:
- Create a key store and a trust store using Java keytool. When creatingthe trust store, you will probably need a client X.509 certificate. Youwill also need to export the server X.509 certificate to include in theclient chain of trust.
- Turn on the SslContextFactory for your Ignite cluster according to thisdocument: Securing Connection Between Nodes.
- Tell Ignite to encrypt data on its thin client port, using the settings forClientConnectorConfiguration. If you only want to encrypt connection,not to validate client’s certificate, set sslClientAuth property tofalse. You’ll still have to set up the trust store on step 1 though.
Client SSL settings is summarized here:
To use the SSL encryption without certificate validation just use_ssl.
To identify the client, create an SSL keypair and a certificate withopenssl command and use them in this manner:
Ignite Create Table Java
To check the authenticity of the server, get the server certificate orcertificate chain and provide its path in the ssl_ca_certfile parameter.
You can also provide such parameters as the set of ciphers (ssl_ciphers) andthe SSL version (ssl_version), if the defaults(
ssl._DEFAULT_CIPHERS and TLS 1.1) do not suit you.
To authenticate you must set authenticationEnabled property to true andenable persistance in Ignite XML configuration file, as described inAuthentication section of Ignite documentation.
Be advised that sending credentials over the open channel is greatlydiscouraged, since they can be easily intercepted. Supplying credentialsautomatically turns SSL on from the client side. It is highly recommendedto secure the connection to the Ignite server, as describedin SSL/TLS example, in order to use password authentication.
Then just supply username and password parameters to
If you still do not wish to secure the connection is spite of the warning,then disable SSL explicitly on creating the client object:
Note, that it is not possible for Ignite thin client to obtain the cluster’sauthentication settings through the binary protocol. Unexpected credentialsare simply ignored by the server. In the opposite case, the user is greetedwith the following message:
Explore a tutorial using Apache Ignite thin client.
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From the version 2.4.0, Apache Ignite introduced a new way to connect to the Ignite cluster, which allows communication with the Ignite cluster without starting an Ignite client node. Historically, Apache Ignite provides two notions of client and server nodes. Ignite client node is intended as lightweight mode, which does not store data (however, it can store near cache) and does not execute any compute tasks. Mainly, the client node is used to communicate with the server remotely and allows manipulating the Ignite Caches using the whole set of Ignite API’s. There are two main downsides with the Ignite Client node:
- Whenever Ignite client node connects to the Ignite cluster, it becomes the part of the cluster topology. The bigger the topology is, the harder it is for maintaining.
- In the client mode, Apache Ignite node consumes a lot of resources for performing cache operations
To solve the above problems, Apache Ignite provides a new binary client protocol for implementing thin Ignite client in any programming language or platforms.
Note that the word thin means it doesn’t start any Ignite node for communicating with the Ignite cluster and doesn’t implement any discovery/communication SPI logic.
Thin client connects to the Ignite cluster through a TCP socket and performs CRUD operations using a well-defined binary protocol. The protocol is a fully socket-based, request-response style protocol. The protocol is designed to be strict enough to ensure standardization in the communication (such as connection handshake, message length, etc.), but still flexible enough that developers may expand upon the protocol to implement custom features.
Portions of this article were taken from the book The Apache Ignite book. If it got you interested, check out the rest of the book for more helpful information. There is a special 20% discount for the DZone readers, please use the following coupon.
Apache Ignite provides brief data formats and communication details in the documentation for using the binary protocol. Ignite already supports .NET, and Java thin client builds on top of the protocol and plans to release a thin client for major languages such as goLang, python, etc. However, you can implement your thin client in any favorite programming language of your choice by using the binary protocol.
Also note that the performance of the Apache Ignite thin client is slightly lower than Ignite client node as it works through an intermediary node. Assume that, you have two nodes of the Apache Ignite A, B and you are using a thin client C for retrieving data from the cluster. With the thin client C, you have connected to the node B, and whenever you try to retrieve any data that belongs to the node A, the requests always go through the client B. In case of the Ignite client node, it sends the request directly to the node A.
Most of the times, you should not care about how the message formats look or the socket handshake performs. Thin client for every programming language encapsulates the ugly hard work under the hood for you. Anyway, if you want to have a deep dive into the Ignite binary protocol or if you have any issues to create with your own thin client, please refer to the Ignite documentation.
Before moving on to more advanced topics, let’s have a look at a simple application to use Ignite thin client. In this simple application, I show you the bits and pieces you need to get started with the thin client. The source code for the examples is available at the GitHub repository, see chapter-2.
Step 1. Clone or download the project from the GitHub repository. If you are planning to develop the project from scratch, add the following maven dependency in your pom.xml file. The only ignite-core library need for the thin client, the rest of the libraries only used for logging.
Step 2. Now, let's create a new Java class with the name HelloThinClient.
Step 3. Copy and paste the following source code. Do not forget to save the file.
Step 4. Let's have a closer look at the program we have written above.
First, we have declared a few constants: logger, host IP address, port, and the cache name that we are going to create. If you have a different IP address, you should change it here. Port 10800 is the default for Ignite thin client.
These are our next exciting lines in the program. We have created an instance of the Ignite СlientConfiguration and passed the address we declared above. In the next try-catch block, we have defined a new cache with name thin-cache and put 2 key-value pairs. We also used the Ignition.start Client method to initialize a connection to Ignite node.
Later, we retrieved the value of key Vladimir and printed the value in the console.
Step 5. Start your Apache Ignite single node cluster if it is not started yet. Use the following command in your favorite terminal.
Step 6. To build the project, issue the following command.
This will run Maven, telling it to execute the install goal. This goal will compile, test, and package your project code and then copy it into the local dependency repository. The first time the build process will take a few minutes to complete, after successful compilation, an executable jar named HelloThinClient-runnable.jar will be created in the target directory.
Step 7. Run the application by typing the following command.
You should see a lot of logs in the terminal. At the end of the log, you should find something like this.
Apache Ignite Create Table
The application is connected through the TCP socket to the Ignite node and performed put and get operations on cache thin-cache. If you take a look at the Ignite node console, you should notice that Ignite cluster topology has not been changed.
Ignite Create Table If Not Exists
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