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C++ Client

GridGain 9 clients connect to the cluster via a standard socket connection. Unlike GridGain 2.x, there is no separate Thin and Thick clients in GridGain 9. All clients are 'thin'.

Clients do not become a part of the cluster topology, never hold any data, and are not used as a destination for compute calculations.

Getting Started

Prerequisites

To run C++ client, you need a C++ build environment to run the cmake command:

  • C++ compiler supporting C++ 17;

  • CMake 3.10+;

  • One of build systems: make, ninja, MS Visual Studio, or other;

  • Conan C/C++ package manager 1.X (optional).

Installation

The source code of the C++ client comes with the GridGain 9 distribution. To build it, use the following commands:

mkdir cmake-build-release
cd cmake-build-release
conan install .. --build=missing -s build_type=Release
cmake ..
cmake --build . -j8
mkdir cmake-build-release
cd cmake-build-release
conan install .. --build=missing -s build_type=Release -s compiler.libcxx=libstdc++11
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . -j8
mkdir cmake-build-release
cd cmake-build-release
conan install .. --build=missing -s build_type=Release -s compiler.libcxx=libc++
cmake .. -DCMAKE_BUILD_TYPE=Release
cmake --build . -j8

Building C++ Client on CentOS 7 and RHEL 7

If you are running on older systems, you need to set up the environment in the following way:

  1. Install epel-release and centos-release-scl:

    yum install epel-release centos-release-scl

  2. Update yum and accept epel-release keys:

    yum update

  3. Install the build tools from the main repository and devtoolset-11:

    yum install devtoolset-11-gcc devtoolset-11-gcc-c++ cmake3 git java-11-openjdk-devel gtest-devel gmock-devel

  4. Install conan version 1.56.0 or more recent but older than 2.00:

    pip3 install -v "conan>=1.56.0,<2.0.0" --force-reinstall

  5. Create and update alternatives for cmake to force the use of cmake3:

    1. Create an alternative for cmake2 with priority 10:

      sudo alternatives --install /usr/local/bin/cmake cmake /usr/bin/cmake 10 \
--slave /usr/local/bin/ctest ctest /usr/bin/ctest \
--slave /usr/local/bin/cpack cpack /usr/bin/cpack \
--slave /usr/local/bin/ccmake ccmake /usr/bin/ccmake \
--family cmake

    2. Create an alternative for cmake3 with priority 20:

      sudo alternatives --install /usr/local/bin/cmake cmake /usr/bin/cmake3 20 \
--slave /usr/local/bin/ctest ctest /usr/bin/ctest3 \
--slave /usr/local/bin/cpack cpack /usr/bin/cpack3 \
--slave /usr/local/bin/ccmake ccmake /usr/bin/ccmake3 \
--family cmake

    3. Check that the default alternative points to cmake3:

      sudo alternatives --config cmake

  6. Enable the devtoolset-11 compiler and start bash with the updated PATH:

    scl enable devtoolset-11 bash

  7. Start the build in the shell you have established.

Client Connector Configuration

Client connection parameters are controlled by the client connector configuration. By default, GridGain accepts client connections on port 10800. You can change the configuration for the node by using the CLI tool at any time.

Here is how the client connector configuration looks like:

"clientConnector": {
  "port": 10800,
  "idleTimeout":3000,
  "sendServerExceptionStackTraceToClient":true,
  "ssl": {
    enabled: true,
    clientAuth: "require",
    keyStore: {
      path: "KEYSTORE_PATH",
      password: "SSL_STORE_PASS"
    },
    trustStore: {
      path: "TRUSTSTORE_PATH",
      password: "SSL_STORE_PASS"
    }
  }
}

The table below covers the configuration for client connector:

Property Default Description

connectTimeout

5000

Connection attempt timeout, in milliseconds.

idleTimeout

0

How long the client can be idle before the connection is dropped,in milliseconds. By default, there is no limit.

metricsEnabled

false

Defines if client metrics are collected.

port

10800

The port the client connector will be listening to.

sendServerExceptionStackTraceToClient

false

Defines if cluster exceptions are sent to the client.

ssl.ciphers

The cipher used for SSL communication.

ssl.clientAuth

Type of client authentication used by clients. For more information, see SSL/TLS.

ssl.enabled

Defines if SSL is enabled.

ssl.keyStore.password

SSL keystore password.

ssl.keyStore.path

Path to the SSL keystore.

ssl.keyStore.type

PKCS12

The type of SSL keystore used.

ssl.trustStore.password

SSL keystore password.

ssl.trustStore.path

Path to the SSL keystore.

ssl.trustStore.type

PKCS12

The type of SSL keystore used.

Here is how you can change the parameters:

node config update clientConnector.port=10469

Connecting to Cluster

To initialize a client, use the IgniteClient class, and provide it with the configuration:

using namespace ignite;

ignite_client_configuration cfg{"127.0.0.1"};
auto client = ignite_client::start(cfg, std::chrono::seconds(5));

Authentication

To pass authentication information, pass it to IgniteClient builder:

auto authenticator = std::make_shared<ignite::basic_authenticator>("myUser", "myPassword");

ignite::ignite_client_configuration cfg{"127.0.0.1:10800"};
cfg.set_authenticator(authenticator);
auto client = ignite_client::start(std::move(cfg), std::chrono::seconds(30));

User Object Serialization

GridGain supports mapping user objects to table tuples. This ensures that objects created in any programming language can be used for key-value operations directly.

Limitations

There are limitations to user types that can be used for such a mapping. Some limitations are common, and others are platform-specific due to the programming language used.

  • Only flat field structure is supported, meaning no nesting user objects. This is because Ignite tables, and therefore tuples have flat structure themselves;

  • Fields should be mapped to Ignite types;

  • All fields in user type should either be mapped to Table column or explicitly excluded;

  • All columns from Table should be mapped to some field in the user type;

  • C++ only: User has to provide marshaling functions explicitly as there is no reflection to generate them based on user type structure.

Usage Examples

struct account {
  account() = default;
  account(std::int64_t id) : id(id) {}
  account(std::int64_t id, std::int64_t balance) : id(id), balance(balance) {}

  std::int64_t id{0};
  std::int64_t balance{0};
};

namespace ignite {

  template<>
  ignite_tuple convert_to_tuple(account &&value) {
    ignite_tuple tuple;

    tuple.set("id", value.id);
    tuple.set("balance", value.balance);

    return tuple;
  }

  template<>
  account convert_from_tuple(ignite_tuple&& value) {
    account res;

    res.id = value.get<std::int64_t>("id");

    // Sometimes only key columns are returned, i.e. "id",
    // so we have to check whether there are any other columns.
    if (value.column_count() > 1)
      res.balance = value.get<std::int64_t>("balance");

    return res;
  }

} // namespace ignite

SQL API

GridGain 9 is focused on SQL, and SQL API is the primary way to work with the data. You can read more about supported SQL statements in the SQL Reference section. Here is how you can send SQL requests:

result_set result = client.get_sql().execute(nullptr, {"select name from tbl where id = ?"}, {std::int64_t{42});
std::vector<ignite_tuple> page = result_set.current_page();
ignite_tuple& row = page.front();

SQL Scripts

The default API executes SQL statements one at a time. If you want to execute large SQL statements, pass them to the executeScript() method. These statements will be executed in order.

std::string script = ""
	+ "CREATE TABLE IF NOT EXISTS Person (id int primary key, city_id int, name varchar, age int, company varchar);"
	+ "INSERT INTO Person (1,3, 'John', 43, 'Sample')";

client.get_sql().execute_script(script);

Transactions

All table operations in GridGain 9 are transactional. You can provide an explicit transaction as a first argument of any Table and SQL API call. If you do not provide an explicit transaction, an implicit one will be created for every call.

Here is how you can provide a transaction explicitly:

auto accounts = table.get_key_value_view<account, account>();

account init_value(42, 16'000);
accounts.put(nullptr, {42}, init_value);

auto tx = client.get_transactions().begin();

std::optional<account> res_account = accounts.get(&tx, {42});
res_account->balance += 500;
accounts.put(&tx, {42}, res_account);

assert(accounts.get(&tx, {42})->balance == 16'500);

tx.rollback();

assert(accounts.get(&tx, {42})->balance == 16'000);

Table API

To execute table operations on a specific table, you need to get a specific view of the table and use one of its methods. You can only create new tables by using SQL API.

When working with tables, you can use built-in Tuple type, which is a set of key-value pairs underneath, or map the data to your own types for a strongly-typed access. Here is how you can work with tables:

Getting a Table Instance

First, get an instance of the table. To obtain an instance of table, use the IgniteTables.table(String) method. You can also use IgniteTables.tables() method to list all existing tables.

using namespace ignite;

auto table_api = client.get_tables();
std::vector<table> existing_tables = table_api.get_tables();
table first_table = existing_tables.front();

std::optional<table> my_table = table_api.get_table("MY_TABLE);

Basic Table Operations

Once you’ve got a table you need to get a specific view to choose how you want to operate table records.

Binary Record View

A binary record view. It can be used to operate table tuples directly.

record_view<ignite_tuple> view = table.get_record_binary_view();

ignite_tuple record{
  {"id", 42},
  {"name", "John Doe"}
};

view.upsert(nullptr, record);
std::optional<ignite_tuple> res_record = view.get(nullptr, {"id", 42});

assert(res_record.has_value());
assert(res_record->column_count() == 2);
assert(res_record->get<std::int64_t>("id") == 42);
assert(res_record->get<std::string>("name") == "John Doe");

Record View

A record view mapped to a user type. It can be used to operate table using user objects which are mapped to table tuples.

record_view<person> view = table.get_record_view<person>();

person record(42, "John Doe");

view.upsert(nullptr, record);
std::optional<person> res_record = view.get(nullptr, person{42});

assert(res.has_value());
assert(res->id == 42);
assert(res->name == "John Doe");

Key-Value Binary View

A binary key-value view. It can be used to operate table using key and value tuples separately.

key_value_view<ignite_tuple, ignite_tuple> kv_view = table.get_key_value_binary_view();

ignite_tuple key_tuple{{"id", 42}};
ignite_tuple val_tuple{{"name", "John Doe"}};

kv_view.put(nullptr, key_tuple, val_tuple);
std::optional<ignite_tuple> res_tuple = kv_view.get(nullptr, key_tuple);

assert(res_tuple.has_value());
assert(res_tuple->column_count() == 2);
assert(res_tuple->get<std::int64_t>("id") == 42);
assert(res_tuple->get<std::string>("name") == "John Doe");

Key-Value View

A key-value view with user objects. It can be used to operate table using key and value user objects mapped to table tuples.

key_value_view<person, person> kv_view = table.get_key_value_view<person, person>();

kv_view.put(nullptr, {42}, {"John Doe"});
std::optional<person> res = kv_view.get(nullptr, {42});

assert(res.has_value());
assert(res->id == 42);
assert(res->name == "John Doe");

Streaming Data

To stream a large amount of data, use the data streamer. Data streaming provides a quicker and more efficient way to load, organize and optimally distribute your data. Data streamer accepts a stream of data and distributes data entries across the cluster, where the processing takes place. Data streaming is available in all table views.

data streaming

Data streaming provides at-least-once delivery guarantee.

Using Data Streamer API

public async Task TestBasicStreamingRecordBinaryView()
{
    var options = DataStreamerOptions.Default with { BatchSize = 10 };
    var data = Enumerable.Range(0, Count).Select(x => new IgniteTuple { ["id"] = 1L, ["name"] = "foo" }).ToList();

    await TupleView.StreamDataAsync(data.ToAsyncEnumerable(), options);
}

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Last updated on Jul 13, 2024