我正在评估Apache Ignite,并尝试了解各种模型如何相互映射。到目前为止,我发现,无论您使用哪种引擎/ API来访问数据,底层存储都是KV对(这是对的吗?)。
现在想到几个问题:
create table a
和create table b
创建了两个缓存,a
和b
?我问这个因为API,当前的API允许您创建一个缓存实例并对其运行多个create table查询。最初我认为这意味着缓存类似于来自RDBMS的DB构造,但是从点燃主分支获取的示例中的注释(如下所示)表示不同。
您可以创建的缓存数量有哪些实际限制,或者您可以继续添加新节点以扩展网格中的缓存数量?
BinaryObject
与SQL表有什么关系?在我看过树的例子中,看来你可以创建一个二进制对象然后通过SQL访问,只要你提供QueryEntity
API的映射。
仅使用BinaryObject
s vs表是否有好处?到目前为止,在我的脑海中,似乎create table应该只映射到底层impl中的二进制对象。使用索引,类型和QueryEntity
映射等内容自动完成。
二进制/表类型(表/缓存名称和列/字段名称)之间的命名限制是什么?我在一个例子中看到,可以在二进制对象上使用a.b
之类的字段作为字段名称,但我不清楚你是如何通过SQL访问它的,因为我相信这样的名称会与现有的语义冲突。
是否有Ignite中可用的各种结构的图表/摘要及其相互之间的关系?看到类似的东西会把我迄今为止所读到的所有内容彻底地带给我。目前正在阅读"使用Ignite"进行高性能的内存计算,尚未完成但是从内容页面到目前为止所阅读的内容我觉得它并没有覆盖一些这些。
最后,鉴于到目前为止可能是一个混乱的理解,我尝试调整其中一个Java示例来结合我提出的大部分问题,但到目前为止还未能使其工作。
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.CacheMode;
import org.apache.ignite.cache.QueryEntity;
import org.apache.ignite.cache.QueryIndex;
import org.apache.ignite.cache.QueryIndexType;
import org.apache.ignite.cache.query.SqlFieldsQuery;
import org.apache.ignite.configuration.CacheConfiguration;
import java.util.Arrays;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
public class TableAndBinaryObjectCacheExperiment {
private static final String cacheName = "some-cache-name";
@SuppressWarnings({"unused", "ThrowFromFinallyBlock"})
public static void main(String[] args) throws Exception {
try (Ignite ignite = Ignition.start("ignite/ignite.xml")) {
if (!ignite.cluster().active()) ignite.cluster().active(true);
// Create dummy cache to act as an entry point for SQL queries (new SQL API which do not require this
// will appear in future versions, JDBC and ODBC drivers do not require it already).
CacheConfiguration<?, ?> cacheCfg = new CacheConfiguration<>(cacheName).setSqlSchema("PUBLIC");
//
LinkedHashMap<String, String> fields = new LinkedHashMap<>();
fields.put("person_id", Long.class.getName());
fields.put("name", String.class.getName());
fields.put("address.postcode", String.class.getName());
fields.put("age", Integer.class.getName());
fields.put("about", String.class.getName());
fields.put("misc", String.class.getName());
QueryEntity testBinType = new QueryEntity();
testBinType.setKeyType(String.class.getName());
testBinType.setValueType("TestType");
//primary key
testBinType.setKeyType(Long.class.getName());
testBinType.setKeyFieldName("test_id");
testBinType.setFields(fields);
testBinType.setTableName("test_type");
testBinType.setIndexes(Arrays.asList(
new QueryIndex("name"),
new QueryIndex("address.postcode"),
new QueryIndex("age"),
new QueryIndex("about", QueryIndexType.FULLTEXT),
new QueryIndex("person_id")
));
CacheConfiguration<?, ?> binaryConf1 = new CacheConfiguration<>(cacheName);
binaryConf1.setCacheMode(CacheMode.PARTITIONED);
binaryConf1.setQueryEntities(Collections.singletonList(testBinType));
//
try (
IgniteCache<?, ?> cache = ignite.getOrCreateCache(cacheCfg);
IgniteCache<?, ?> binCacheX = ignite.getOrCreateCache(binaryConf1)
) {
IgniteCache<?, ?> binCache = cache.withKeepBinary();
// Create reference City table based on REPLICATED template.
cache.query(new SqlFieldsQuery("CREATE TABLE IF NOT EXISTS city (id LONG PRIMARY KEY, name VARCHAR) WITH \"template=replicated\"")).getAll();
// Create table based on PARTITIONED template with one backup.
cache.query(new SqlFieldsQuery("CREATE TABLE IF NOT EXISTS person (id LONG, name VARCHAR, city_id LONG, PRIMARY KEY (id, city_id)) WITH \"backups=1, affinity_key=city_id\"")).getAll();
// Create an index.
cache.query(new SqlFieldsQuery("CREATE INDEX IF NOT EXISTS on Person (city_id)")).getAll();
print("Created database objects.");
SqlFieldsQuery qry = new SqlFieldsQuery("INSERT INTO city (id, name) VALUES (?, ?)");
cache.query(qry.setArgs(1L, "Forest Hill")).getAll();
cache.query(qry.setArgs(2L, "Denver")).getAll();
cache.query(qry.setArgs(3L, "St. Petersburg")).getAll();
qry = new SqlFieldsQuery("INSERT INTO person (id, name, city_id) values (?, ?, ?)");
cache.query(qry.setArgs(1L, "John Doe", 3L)).getAll();
cache.query(qry.setArgs(2L, "Jane Roe", 2L)).getAll();
cache.query(qry.setArgs(3L, "Mary Major", 1L)).getAll();
cache.query(qry.setArgs(4L, "Richard Miles", 2L)).getAll();
qry = new SqlFieldsQuery("INSERT INTO test_type (test_id, name, age, about, \"address.postcode\") values (? ?, ?, ?, ?)");
cache.query(qry.setArgs(1L, "Courtney", 12, "this is about me", "AB12CD", 3L));
SqlFieldsQuery joinQuery = new SqlFieldsQuery(
"SELECT p.name, c.name, t.about, \"t.address.postcode\" " +
"FROM Person p " +
"INNER JOIN City c on c.id = p.city_id " +
"INNER JOIN test_type t on p.id = t.person_id " +
"LIMIT 50");
List<List<?>> res = cache.query(joinQuery).getAll();
for (Object next : res)
System.out.println(">>> " + next);
} finally {
// Distributed cache can be removed from cluster only by #destroyCache() call.
ignite.destroyCache(cacheName);
}
print("Cache query DDL example finished.");
}
}
/**
* Prints message.
*
* @param msg Message to print before all objects are printed.
*/
private static void print(String msg) {
System.out.println();
System.out.println(">>> " + msg);
}
}
我已阅读过这些文档,但要么错过了这些信息,要么他们不会立即显现或存在。
答案 0 :(得分:2)
SQL_PUBLIC_A
和SQL_PUBLIC_B
。一个缓存 - 一个表。 SQL查询当前需要在Native API中调用缓存,这是一个遗留限制,将来会被解除。运行调用查询的缓存无关紧要。您可以拥有一些默认缓存,在其上运行所有CREATE
和SELECT
。CacheGroup
允许具有相同结构的不同缓存共享大多数基础结构,节省开销。BinaryObject
表示。这意味着您始终可以访问BinaryObject
个行。如果您在创建缓存时提供了映射,则还可以将BinaryObject
作为行访问。Cache API
与BinaryObject
一起使用,包括IgniteDataStreamer
。 Ignite有大量的API,其中大部分都可以通过BinaryObject
访问,但只能通过表访问SQL查询。所以BinaryObject
是您的编组工具。BinaryObject
是所有功能的超集,因此只有BinaryObject
的一部分可能对SQL引擎有意义。现在,修复代码示例需要单独响应。
答案 1 :(得分:1)
import org.apache.ignite.Ignite;
import org.apache.ignite.IgniteCache;
import org.apache.ignite.Ignition;
import org.apache.ignite.cache.CacheMode;
import org.apache.ignite.cache.QueryEntity;
import org.apache.ignite.cache.QueryIndex;
import org.apache.ignite.cache.QueryIndexType;
import org.apache.ignite.cache.query.SqlFieldsQuery;
import org.apache.ignite.configuration.CacheConfiguration;
import java.util.Arrays;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
public class TableAndBinaryObjectCacheExperiment {
private static final String cacheName = "some-cache-name";
@SuppressWarnings({"unused", "ThrowFromFinallyBlock"})
public static void main(String[] args) throws Exception {
try (Ignite ignite = Ignition.start("ignite/ignite.xml")) {
if (!ignite.cluster().active()) ignite.cluster().active(true);
// Create dummy cache to act as an entry point for SQL queries (new SQL API which do not require this
// will appear in future versions, JDBC and ODBC drivers do not require it already).
CacheConfiguration<?, ?> cacheCfg = new CacheConfiguration<>("default").setSqlSchema("PUBLIC");
//
LinkedHashMap<String, String> fields = new LinkedHashMap<>();
fields.put("person_id", Long.class.getName());
fields.put("name", String.class.getName());
fields.put("address_postcode", String.class.getName());
fields.put("age", Integer.class.getName());
fields.put("about", String.class.getName());
fields.put("misc", String.class.getName());
QueryEntity testBinType = new QueryEntity();
testBinType.setValueType("TestType");
//primary key
testBinType.setKeyType(Long.class.getName());
testBinType.setKeyFieldName("person_id");
testBinType.setFields(fields);
testBinType.setTableName("test_type");
testBinType.setIndexes(Arrays.asList(
new QueryIndex("name"),
new QueryIndex("address_postcode"),
new QueryIndex("age"),
new QueryIndex("about", QueryIndexType.FULLTEXT)
));
CacheConfiguration<?, ?> binaryConf1 = new CacheConfiguration<>(cacheName);
binaryConf1.setCacheMode(CacheMode.PARTITIONED);
binaryConf1.setQueryEntities(Collections.singletonList(testBinType));
//
try (
IgniteCache<?, ?> cache = ignite.getOrCreateCache(cacheCfg);
IgniteCache<?, ?> binCacheX = ignite.getOrCreateCache(binaryConf1)
) {
IgniteCache<?, ?> binCache = cache.withKeepBinary();
// Create reference City table based on REPLICATED template.
cache.query(new SqlFieldsQuery("CREATE TABLE IF NOT EXISTS city (id LONG PRIMARY KEY, name VARCHAR) WITH \"template=replicated\"")).getAll();
// Create table based on PARTITIONED template with one backup.
cache.query(new SqlFieldsQuery("CREATE TABLE IF NOT EXISTS person (id LONG, name VARCHAR, city_id LONG, PRIMARY KEY (id, city_id)) WITH \"backups=1, affinity_key=city_id\"")).getAll();
// Create an index.
cache.query(new SqlFieldsQuery("CREATE INDEX IF NOT EXISTS on Person (city_id)")).getAll();
print("Created database objects.");
SqlFieldsQuery qry = new SqlFieldsQuery("INSERT INTO city (id, name) VALUES (?, ?)");
cache.query(qry.setArgs(1L, "Forest Hill")).getAll();
cache.query(qry.setArgs(2L, "Denver")).getAll();
cache.query(qry.setArgs(3L, "St. Petersburg")).getAll();
qry = new SqlFieldsQuery("INSERT INTO person (id, name, city_id) values (?, ?, ?)");
cache.query(qry.setArgs(1L, "John Doe", 3L)).getAll();
cache.query(qry.setArgs(2L, "Jane Roe", 2L)).getAll();
cache.query(qry.setArgs(3L, "Mary Major", 1L)).getAll();
cache.query(qry.setArgs(4L, "Richard Miles", 2L)).getAll();
qry = new SqlFieldsQuery("INSERT INTO \"some-cache-name\".test_type (person_id, name, age, about, address_postcode) values (?, ?, ?, ?, ?)");
cache.query(qry.setArgs(1L, "Courtney", 12, "this is about me", "AB12CD", 3L));
SqlFieldsQuery joinQuery = new SqlFieldsQuery(
"SELECT p.name, c.name, t.about, t.address_postcode " +
"FROM Person p " +
"INNER JOIN City c on c.id = p.city_id " +
"INNER JOIN \"some-cache-name\".test_type t on p.id = t.person_id " +
"LIMIT 50");
List<List<?>> res = cache.query(joinQuery).getAll();
for (Object next : res)
System.out.println(">>> " + next);
} finally {
// Distributed cache can be removed from cluster only by #destroyCache() call.
ignite.destroyCache(cacheName);
}
print("Cache query DDL example finished.");
}
}
/**
* Prints message.
*
* @param msg Message to print before all objects are printed.
*/
private static void print(String msg) {
System.out.println();
System.out.println(">>> " + msg);
}
}
现在好多了:
>>> Created database objects.
>>> [John Doe, St. Petersburg, this is about me, AB12CD]
>>> Cache query DDL example finished.
答案 2 :(得分:0)
BinaryObject是Ignite的数据序列化和数据存储格式。您可能会将其视为JSON,它是针对Ignite特性进行优化和设计的。
格式的好处是您可以在服务器节点端运行计算和其他操作,从而避免对其类表单进行数据反序列化。
谈到SQL,您通过INSERTS,UPDATES等添加到集群的所有数据也将以BinaryObject形式存储。
如果您想混合SQL,键值和计算API,那么您可以使用CREATE TABLE命令创建表/缓存,使用CACHE_NAME参数将缓存名称覆盖为您喜欢的名称。稍后,使用缓存名称作为键值,计算网格和其他操作。请参阅显示API如何混合的this project。