Java Edition Hack Client

[Slikk Huisenga]

TheJDK includes two flavors of the VM -- a client-side offering, and a VM tuned for server applications. These two solutions share the Java HotSpot runtime environment code base, but use different compilers that are suited to the distinctly unique performance characteristics of clients and servers. These differences include the compilation inlining policy and heap defaults.

Although the Server and the Client VMs are similar, the Server VM has been specially tuned to maximize peak operating speed. It is intended for executing long-running server applications, which need the fastest possible operating speed more than a fast start-up time or smaller runtime memory footprint.

The Client VM compiler serves as an upgrade for both the Classic VM and the just-in-time (JIT) compilers used by previous versions of the JDK. The Client VM offers improved run time performance for applications and applets. The Java HotSpot Client VM has been specially tuned to reduce application start-up time and memory footprint, making it particularly well suited for client environments. In general, the client system is better for GUIs.

The Client VM compiler does not try to execute many of the more complex optimizations performed by the compiler in the Server VM, but in exchange, it requires less time to analyze and compile a piece of code. This means the Client VM can start up faster and requires a smaller memory footprint.

The Server VM contains an advanced adaptive compiler that supports many of the same types of optimizations performed by optimizing C++ compilers, as well as some optimizations that cannot be done by traditional compilers, such as aggressive inlining across virtual method invocations. This is a competitive and performance advantage over static compilers. Adaptive optimization technology is very flexible in its approach, and typically outperforms even advanced static analysis and compilation techniques.

Note: The release of jdk6 update 10 (see Update Release Notes:Changes in 1.6.0_10) tried to improve startup time, but for a different reason than the hotspot options, being packaged differently with a much smaller kernel.

These values can change for different combinations of architecture, operating system and jvm version however. Recent versions of the jvm have removed flags and re-moved many of the distinctions between server and client.

Remember too that you can see all the details of a running jvm using jvisualvm. This is useful if you have users who or modules which set JAVA_OPTS or use scripts which change command line options. This will also let you monitor, in real time, heap and permgen space usage along with lots of other stats.

the -client and -server systems are different binaries. They are essentially two different compilers (JITs) interfacing to the same runtime system. The client system is optimal for applications which need fast startup times or small footprints, the server system is optimal for applications where the overall performance is most important. In general the client system is better suited for interactive applications such as GUIs

One difference I've just noticed is that in "client" mode, it seems the JVM actually gives some unused memory back to the operating system, whereas with "server" mode, once the JVM grabs the memory, it won't give it back. Thats how it appears on Solaris with Java6 anyway (using prstat -Z to see the amount of memory allocated to a process).

IIRC the server VM does more hotspot optimizations at startup so it runs faster but takes a little longer to start and uses more memory. The client VM defers most of the optimization to allow faster startup.

I've not noticed any difference in startup time between the 2, but clocked a very minimal improvement in application performance with "-server" (Solaris server, everyone using SunRays to run the app). That was under 1.5.

If you can compare two java VMs, one client, one server using the jvisualvm tool, you should see a difference in the frequency and effect of the garbage collection, as well as in the number of generations.

I had a pair of screenshots that showed the difference really well, but I can't reproduce as I have a 64 bit JVM which only implements the server VM. (And I can't be bothered to download and wrangle the 32 bit version on my system as well.)

When doing a migration from 1.4 to 1.7("1.7.0_55") version.The thing that we observed here is, there is no such differences in default values assigned to heapsizepermsizeThreadStackSize parameters in client & server mode.

ThreadStackSize is higher in 1.7, while going through Open JDK forum,there are discussions which stated frame size is somewhat higher in 1.7 version.It is believed real difference could be possible to measure at run time based on your behavior of your application

Welcome to the Hazelcast IMDG (In-Memory Data Grid) Reference Manual.This manual includes concepts, instructions and examples to guide you onhow to use Hazelcast and build Hazelcast IMDG applications.

This reference manual mostly talks about the Hazelcast member and clientsin Java language. Although, the core of Hazelcast IMDG is based on the Javaprogramming language, it has the following clients and programming language APIs.

Hazelcast or Hazelcast IMDG refers to the open source editionof Hazelcast in-memory data grid middleware. Hazelcast is also thename of the company (Hazelcast, Inc.) providing the Hazelcast product.

Hazelcast IMDG Pro is a commercially licensed edition ofHazelcast IMDG which provides CP Sub-system Persistence, Hot Restart Persistence, Unlimited Members on Hazelcast Management Center (This is restricted to 3 members on the freely available but unlicensed version of Management Center) & 24/7 support.

Hazelcast IMDG Enterprise is a commercially licensed edition ofHazelcast IMDG which provides all the features of the Pro Edition, plus High-Density Memory Store, Security Suite (RBAC, TLS, Mutual Auth, Client Certificates), WAN Replication (Sync data between two geo-replicated clusters), Blue/Green Deployments, Rolling Upgrades, Automatic Disaster Recovery for Clients & 24/7 supports.

Hazelcast partners with leading hardware and software technologies, system integrators, resellersand OEMs including Amazon Web Services, Vert.x, Azul Systems, C2B2. See thePartners page for the full list of and information on our partners.

The Algolia Java API client requires Java 1.8 or later for all modules except algoliasearch-java-net.The algoliasearch-java-net module requires Java 11 or later.Your JDK must support TLS version 1.2 or later.

MariaDB Connector/J is a Type 4 JDBC driver. It was developed specifically asa lightweight JDBC connector for use with MariaDB and MySQL database servers.It was originally based on the Drizzle JDBC code with numerous additions andbug fixes.

The following subsections show the formatting of JDBC connection strings forMariaDB and MySQL database servers. Additionally, sample code is provided thatdemonstrates how to connect to one of these servers and create a table.

The preferred way to get a connection with MariaDB Connector/J is to use the DriverManager class.When the DriverManager class is used to locate and load MariaDB Connector/J, the application needs no further configuration. The DriverManager class will automatically load MariaDB Connector/J and allow it to be used in the same way as any other JDBC driver.

MariaDB Connector/J 3.0 only accepts jdbc:mariadb: as the protocol in connection strings by default. When both MariaDB Connector/J and the MySQL drivers are found in the class-path, using jdbc:mariadb: as the protocol helps to ensure that Java chooses MariaDB Connector/J.

driver 3.0 is a complete rewrite of the connector. Specific support for aurora has not been implemented in 3.0, since it relies on pipelining. Aurora is not compatible with pipelining.Issues for Aurora were piling up without the community proposing any PR for them and without access for us to test those modifications. (2.x version has a 5 years support).

GSSAPI in windows isn't well supported in java, causing recurrent issues. Since 3.1, waffle-jna is marked as a dependency to provide good GSSAPI support without problems. This has the drawback to make connector and dependencies to a size of around 4Mb.

By default, the connector adopts the JVM's default time zone. If the client and server reside in different time zones, it's recommended to configure the connection time zone to match the JVM's default by setting forceConnectionTimeZoneToSession to true.This ensures proper operation of time functions.

Due to its wider range, DATETIME is sometimes mistakenly used to store a specific point in time. While this might work if the client and server share the same time zone, it creates problems when they differ.

While using DATETIME instead of TIMESTAMP is generally discouraged, a specific combination of settings ("preserveInstants=true&connectionTimeZone=SERVER") can force all Java Timestamp exchanges to be converted to the connection's time zone during storage and retrieval. However, this approach is not recommended for long-term solutions.

The MariaDB Connector/J versions before 3.4 offered a single "timezone" option. While this functionality remains compatible, it's now separated into two distinct settings: connectionTimeZone and forceConnectionTimeZoneToSession. Here's a breakdown of how the old option translates to the new ones:"timezone=America/Los_Angeles" is equivalent to "connectionTimeZone=America/Los_Angeles&forceConnectionTimeZone=true

The fastest way to load lots of data is using LOAD DATA INFILE.

However, using "LOAD DATA LOCAL INFILE" (ie: loading a file from the client) may be a security problem if someone can execute a query from the client, he can have access to any file on the client (according to the rights of the user running the client process).

To avoid using too much memory, rather use Statement.setFetchSize(int numberOfRowInMemory) to indicate the number of rows that will be stored in memory

Example :

using Statement.setFetchSize(1000) indicates that 1000 rows will be stored in memory.

So, when the query has executed, 1000 rows will be in memory. After 1000 ResultSet.next(), the next 1000 rows will be stored in memory, and so on.

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