Preface
Hibernate Reactive is a reactive API for Hibernate ORM, supporting non-blocking database drivers and a reactive style of interaction with the database.
Hibernate Reactive is intended for use in a reactive programming environment like Vert.x, where interaction with the
database should occur in a non-blocking fashion. Persistence operations are orchestrated via the construction of a
reactive stream rather than via direct invocation of synchronous functions in procedural Java code. The reactive stream
is represented using a chain of Java CompletionStage
s or Mutiny Uni
s and Multi
s.
Java persistence frameworks like JDBC, JPA and Hibernate ORM were designed to use blocking IO for interaction with the database, and are therefore not appropriate for use in a reactive environment. As far as we know, Hibernate Reactive is the first true ORM implementation designed to take advantage of non-blocking database clients. Out of the box, the Vert.x clients for PostgreSQL, MySQL, and DB2 are supported, though the architecture is not limited to these drivers.
This programming paradigm holds the potential for improved scalability, and more controlled degradation under peak load, in some runtime scenarios. However, in general, one should not expect to see an immediate performance gain in all performance tests. Indeed, many programs will not benefit from the programming model, and those which do benefit might only benefit in very specific load scenarios.
1. Introduction to Hibernate Reactive
Creating a new project with Hibernate Reactive isn’t hard at all. In this short guide, we’ll cover all the basic work involved in:
-
setting up and configuring a project, and then
-
writing Java code to define a data model and access the database.
Last, we’ll discuss some topics related to performance that you’ll need to understand when you develop any large-scale project using Hibernate.
But, before you start, we recommend taking a quick look at the very
simplistic example program in the session-example
directory,
which shows off all the "bits" you’ll need to get your own program up and
running.
1.1. Information about Hibernate ORM
This document assumes some passing familiarity with Hibernate ORM or another implementation of JPA. If you’ve never used JPA before, that’s OK, but you might need to refer to the following sources of information at some points in this text:
-
the JPA 2.2 specification, or
-
Java Persistence with Hibernate, the latest edition of the book originally titled Hibernate in Action.
1.2. Setting up a reactive Hibernate project
If you’re using Hibernate Reactive outside of the Quarkus environment, you’ll need to:
-
include Hibernate Reactive itself, along with the appropriate Vert.x reactive database client, as dependencies of your project, and
-
configure Hibernate Reactive with information about your database, using Hibernate configuration properties.
Or, if you want to use Hibernate Reactive in Quarkus, you can generate a preconfigured skeleton project right here.
1.2.1. Including Hibernate Reactive in your project build
Add the following dependency to your project:
org.hibernate.reactive:hibernate-reactive-core:{version}
Where {version}
is the version of Hibernate Reactive you’re using.
You’ll also need to add a dependency for the Vert.x reactive database driver for your database, one of the following options:
| Database | Driver dependency |
|---|---|
PostgreSQL or CockroachDB |
|
MySQL or MariaDB |
|
DB2 |
|
SQL Server |
|
Where {vertxVersion}
is the version of Vert.x compatible with the
version of Hibernate Reactive you’re using.
You don’t need to depend on the JDBC driver for your database.
1.2.2. Optional dependencies
Optionally, you might also add any of the following additional features:
| Optional feature | Dependencies |
|---|---|
An SLF4J logging implementation |
|
The Hibernate metamodel generator, if you’re using the JPA criteria query API |
|
Hibernate Validator |
|
Compile-time checking for your HQL queries |
|
Second-level cache support via JCache and EHCache |
|
SCRAM authentication support for PostgreSQL |
|
You might also add the Hibernate bytecode enhancer to your Gradle build if you want to use field-level lazy fetching.
| Field-level lazy fetching is an advanced feature that most programs don’t need. Stick to the basics for now. |
There’s an example Gradle build included in the example program.
1.2.3. Basic configuration
Hibernate Reactive is configured via the standard JPA persistence.xml
document which must be placed, as usual, in the /META-INF
directory.
An example persistence.xml
file is included in the example
program.
The only required configuration that’s really specific to Hibernate
Reactive is the persistence <provider>
element, which must be explicit:
<provider>org.hibernate.reactive.provider.ReactivePersistenceProvider</provider>
Otherwise, configuration is almost completely transparent—you can configure Hibernate Reactive pretty much exactly as you would usually configure Hibernate ORM core.
A full list of configuration properties recognized by Hibernate may be found in the documentation for Hibernate ORM. You’ll never need to touch most of these. The properties you do need at this stage are these three:
| Configuration property name | Purpose |
|---|---|
|
JDBC URL of your database |
|
Your database credentials |
These configuration properties have jdbc
in their names, but of course
there’s no JDBC in Hibernate Reactive, and these are simply the legacy
property names defined by the JPA specification. In particular, Hibernate
Reactive itself parses and interprets the JDBC URL.
You don’t need to specify hibernate.dialect . The correct Hibernate
Dialect will be determined for you by Hibernate Reactive.
|
The Vert.x database client has built-in connection pooling and prepared statement caching. You might want to control the size of the connection pool:
| Configuration property name | Purpose |
|---|---|
|
The maximum size of the reactive connection pool |
We’ll learn about more advanced connection pool tuning later, in Tuning the Vert.x pool.
| Hibernate has many configurable things, but many exist only to maintain compatibility with legacy code, and most configuration properties directly related to JDBC or JTA aren’t relevant in the context of Hibernate Reactive. |
1.2.4. Automatic schema export
You can have Hibernate Reactive infer your database schema from the mapping annotation you’ve specified in your Java code, and export the schema at initialization time by specifying one or more of the following configuration properties:
| Configuration property name | Purpose |
|---|---|
|
|
|
(Optional) If |
|
(Optional) If |
|
(Optional) The name of the SQL script to be executed |
This feature is extremely useful for testing.
Hibernate Reactive doesn’t support validate and update with Db2.
|
Schema export uses blocking operations so starting the factory might require special handling when using it. Failing to do so will cause an exception:
io.vertx.core.VertxException: Thread blocked
You can solve this issue using executeBlocking
:
Vertx vertx = ...
Uni<Void> startHibernate = Uni.createFrom().deferred(() -> {
emf = Persistence
.createEntityManagerFactory("demo")
.unwrap(Mutiny.SessionFactory.class);
return Uni.createFrom().voidItem();
});
startHibernate = vertx.executeBlocking(startHibernate)
.onItem().invoke(() -> logger.info("✅ Hibernate Reactive is ready"));
1.2.5. Logging the generated SQL
To see the generated SQL as it’s sent to the database, either:
-
set the property
hibernate.show_sql
totrue
, or -
enable debug-level logging for the category
org.hibernate.SQL
using your preferred SLF4J logging implementation.
For example, if you’re using Log4J 2 (as above in Optional dependencies),
add these lines to your log4j2.properties
file:
logger.hibernate.name = org.hibernate.SQL
logger.hibernate.level = debug
An example log4j2.properties
file is included in the example
program.
You can make the logged SQL more readable by enabling one or both of the following settings:
| Configuration property name | Purpose |
|---|---|
|
If |
|
If |
1.2.6. Minimizing repetitive mapping information
The following properties are very useful for minimizing the amount of
information you’ll need to explicitly specify in @Table
and @Column
annotations which we’ll discuss below in Mapping entity classes:
| Configuration property name | Purpose |
|---|---|
|
A default schema name for entities which do not explicitly declare one |
|
A default catalog name for entities which do not explicitly declare one |
|
A |
Writing your own PhysicalNamingStrategy is an especially good
way to reduce the clutter of annotations on your entity classes, and
we think you should do it for any nontrivial data model.
|
1.3. Writing the Java code
With that out of the way, we’re all set to write some Java code!
As is the case in any project that uses Hibernate, your persistence-related code comes in two main pieces:
-
a representation of your data model in Java, which takes the form of a set of annotated entity classes, and
-
a larger number of functions which interact with Hibernate’s APIs to perform the persistence operations associated with your various transactions.
The first part, the data or "domain" model, is usually easier to write, but doing a great and very clean job of it will strongly affect your success in the second part.
Take your time with this code, and try to produce a Java model
that’s as close as reasonable to the relational data model. Avoid using
exotic or advanced mapping features when they’re not really needed.
When in the slightest doubt, map a foreign key relationship using
@ManyToOne with @OneToMany(mappedBy=…) in preference to more
complicated association mappings.
|
The second part of the code is much trickier to get right. This code must:
-
manage transactions and reactive sessions,
-
construct reactive streams by chaining persistence operations invoked on the reactive session,
-
fetch and prepare data needed by the UI, and
-
handle failures.
| Some responsibility for transaction and session management, and for recovery from certain kinds of failure, can be best handled in some sort of framework code. |
1.3.1. Mapping entity classes
We won’t have much to say about the entity classes here, simply because the principles behind mapping entity classes in Hibernate Reactive, along with the actual mapping annotations you’ll use, are all identical to regular Hibernate ORM and other implementations of JPA.
For example:
@Entity
@Table(name="authors")
class Author {
@Id @GeneratedValue
private Integer id;
@NotNull @Size(max=100)
private String name;
@OneToMany(mappedBy = "author", cascade = PERSIST)
private List<Book> books = new ArrayList<>();
Author(String name) {
this.name = name;
}
Author() {}
// getters and setters...
}
You’re quite free to mix and match:
-
the regular JPA mapping annotations defined in the package
javax.persistence
with -
the advanced mapping annotations in
org.hibernate.annotations
, and even -
annotations like
@NotNull
and@Size
defined by Bean Validation.
A full list of object/relational mapping annotations may be found in the documentation for Hibernate ORM. Most mapping annotations are already supported in Hibernate Reactive, though there are still a handful of limitations at this time.
Common JPA annotations
The most common and useful mapping annotations include these standard JPA annotations:
| Annotation | Purpose |
|---|---|
|
Declares an entity class (a class with its own database table an persistent identity) |
|
A superclass that declares common persistent
fields of its |
|
Declare an embeddable class (a class without its own persistent identity or database table) |
|
Defines how inheritance hierarchies should be mapped to database tables |
|
Specifies that a field of an entity holds the persistent identity of the entity, and maps to the primary key of its table |
|
Specifies a class representing the composite
primary key of the entity (for entities with
multiple |
|
Specifies that a field of an entity holds its
composite primary key represented as an
|
|
Specifies that an identifier is a system-generated surrogate key |
|
Specifies that a field of an entity holds a version number used for optimistic locking |
|
Maps a field holding an |
|
Declares a many-to-one association to a second entity |
|
Declares a one-to-one association to a second entity |
|
Declares a one-to-many association to a second entity |
|
Specifies a mapping to a database table |
|
Specifies a mapping to a second database table |
|
Specifies a mapping to a database column |
|
Specifies a mapping to a database foreign key |
Useful Hibernate annotations
These Hibernate annotations are also quite useful to know about:
| Annotation | Purpose |
|---|---|
|
Enables second-level caching for an entity |
|
Maps field to SQL expression instead of a column |
|
Automatically assign a timestamp to a field |
|
Enables optimistic locking for entities with no
|
|
Define a Hibernate filter |
|
Defines a Hibernate fetch profile |
|
Defines a property generated by the database |
|
Specifies a SQL expression used to assign a
default value to a column.
(Use in combination with |
|
Selects a custom id generator |
|
Generate SQL dynamically with only needed columns (instead of using static SQL generated at startup) |
|
Specifies the fetching mode for an association |
|
Specifies the batch size for batch fetching an association |
|
Specify custom DML for entity operations |
|
Marks a field or fields as an alternative "natural" identifier (unique key) of the entity |
Bean Validation annotations
Information about Bean Validation annotations may be found in the documentation for Hibernate Validator.
for defining a required field, we prefer to use the @NotNull
annotation from Bean Validation instead of JPA’s more verbose
@Basic(optional=false) . Similarly, we prefer to define the length of a
text field using @Size(100) rather than @Column(length=100) .
|
1.3.2. Getters and setters
When using Hibernate Reactive outside the Quarkus environment, you’ll need to write your entity classes according to the usual JPA conventions, which require:
-
private fields for persistent attributes, and
-
a nullary constructor.
It’s illegal to access persistent fields from outside the entity class. Therefore, external access to persistent fields must be intermediated via getter and setter methods defined by the entity class.
If you access fields of an unfetched entity instance from code
outside the entity class, you’ll obtain bogus null or default (zero)
values!
|
When you use Hibernate Reactive in Quarkus, these requirements are relaxed, and you can use public fields instead of getters and setters if you prefer.
1.3.3. equals()
and hashCode()
Entity classes should override equals()
and hashCode()
. People new to
Hibernate or JPA are often confused by exactly which fields should be
included in the hashCode()
, so please keep the following principles in
mind:
-
You should not include mutable fields in the hashcode, since that would require rehashing any collection containing the entity whenever the field is mutated.
-
It’s not completely wrong to include a generated identifier (surrogate key) in the hashcode, but since the identifier is not generated until the entity instance is made persistent, you must take great care to not add it to any hashed collection before the identifier is generated. We therefore advise against including any database-generated field in the hashcode.
It’s OK to include any immutable, non-generated field in the hashcode.
We therefore recommend identifying a natural key for each entity,
that is, a combination of fields that uniquely identifies an instance of
the entity, from the perspective of the data model of the program. The
business key should correspond to a unique constraint on the database,
and to the fields which are included in equals() and hashCode() .
|
That said, an implementation of equals()
and hashCode()
based on the
generated identifier of the entity can work if you’re careful.
| If you can’t identify a natural key, it might be a sign that you need to think more carefully about some aspect of your data model. If an entity doesn’t have a meaningful unique key, then it’s impossible to say what event or object it represents in the "real world" outside your program. |
Note that even when you’ve identified a natural key, we still recommend the use of a generated surrogate key in foreign keys, since this makes your data model much easier to change.
1.3.4. Identifier generation
One area where the functionality of Hibernate Reactive diverges from plain Hibernate is in the area of id generation. Custom identifier generators written to work with Hibernate ORM and JDBC will not work in the reactive environment.
-
Sequence, table, and
UUID
id generation is built in, and these id generation strategies may be selected using the usual JPA mapping annotations:@GeneratedValue
,@TableGenerator
,@SequenceGenerator
. -
On MySQL, an autoincrement column may be used by specifying
@GeneratedValue(strategy=GenerationType.IDENTITY)
-
Custom id generators may be defined by implementing
ReactiveIdentifierGenerator
and declaring the custom implementation using@GenericGenerator
. -
Natural ids—including composite ids—may be assigned by the program in the usual way.
The standard id generation strategies defined by the JPA specification may be customized using the following annotations:
| Annotation | Purpose |
|---|---|
|
Configure a generator based on a database sequence |
|
Configure a generator based on a row of a database table |
For example, sequence id generation may be specified like this:
@Entity
@Table(name="authors")
class Author {
@Id @GeneratedValue(generator = "authorIds")
@SequenceGenerator(name = "authorIds",
sequenceName = "author_ids",
allocationSize = 20)
Integer id;
...
}
You can find more information in the JPA specification.
If you have very particular requirements, you can check out the Javadoc of
ReactiveIdentifierGenerator
for information on how to implement your own
custom reactive identifier generator.
1.3.5. Custom types
Hibernate custom types based on the UserType
interface are targeted toward
use with JDBC, and depend on interfaces defined by JDBC. So Hibernate Reactive
features an adaptor that exposes a partial implementation of JDBC to the
UserType
implementation.
Therefore, some existing UserType
implementations will work in Hibernate
Reactive, depending upon precisely which features of JDBC they depend on.
| Where possible, use a JPA attribute converter instead of a custom type, since attribute converters are not in any way tied to JDBC. |
You may specify a custom type by annotating a field of an entity class with
the Hibernate @Type
annotation.
1.3.6. Attribute converters
Any JPA AttributeConverter
works in Hibernate Reactive. For example:
@Converter
public class BigIntegerAsString implements AttributeConverter<BigInteger, String> {
@Override
public String convertToDatabaseColumn(BigInteger attribute) {
return attribute == null ? null : attribute.toString(2);
}
@Override
public BigInteger convertToEntityAttribute(String string) {
return string == null ? null : new BigInteger(string, 2);
}
}
You’ll need to use one or both of these annotations:
| Annotation | Purpose |
|---|---|
|
Declares a class implementing |
|
Specifies an |
You’ll find more information in the Javadoc for these annotations and in the JPA specification.
1.3.7. APIs for chaining reactive operations
When you write persistence logic using Hibernate Reactive, you’ll be working
with a reactive Session
most of the time. Just to make things a little more
confusing for new users, the reactive Session
and its related interfaces all
come in two flavors:
-
Stage.Session
and friends provide a reactive API based around Java’sCompletionStage
, and -
Mutiny.Session
and friends provide an API based on Mutiny.
You’ll need to decide which API you want to use!
If you take the time to look over the types Stage.Session and
Mutiny.Session , you’ll notice they’re almost identical. Choosing between
them is a matter of deciding which reactive API you want to use for working
with reactive streams. Your decision won’t affect what you can do with
Hibernate Reactive. On the other hand, we’ve sent a lot of feedback and
requests for improvement to the Mutiny team, and we think it’s now the case
that Hibernate Reactive code is simpler and cleaner with Mutiny.
|
These are the most important operations on reactive streams that you’ll need all the time when working with Hibernate Reactive:
| Purpose | Java CompletionStage |
Mutiny Uni |
|---|---|---|
Chain non-blocking operations |
|
|
Transform streamed items |
|
|
Perform an action using streamed items |
|
|
Perform cleanup (similar to |
|
|
In this introduction, our code examples usually use Mutiny. If you’re more
familiar with CompletionStage
, you can refer to the above table to help
you understand the code.
When we use the term reactive stream in this document, we mean:
-
a chain of
CompletionStage
s, or -
a chain of Mutiny
Uni
s andMulti
s
that is built by the program in order to service a particular request, transaction, or unit of work.
1.3.8. Obtaining a reactive session factory
Whatever you decide, the first step to getting a reactive session is to obtain
a JPA EntityManagerFactory
just as you usually would in plain ol' regular JPA,
for example, by calling:
EntityManagerFactory emf = Persistence.createEntityManagerFactory("example");
Now, unwrap()
the reactive SessionFactory
. If you want to use
CompletionStage
s for chaining reactive operations, ask for a
Stage.SessionFactory
:
Stage.SessionFactory sessionFactory = emf.unwrap(Stage.SessionFactory.class);
Or, if you prefer to use the Mutiny-based API, unwrap()
the type
Mutiny.SessionFactory
:
Mutiny.SessionFactory sessionFactory = emf.unwrap(Mutiny.SessionFactory.class);
Reactive sessions may be obtained from the resulting reactive SessionFactory
.
It’s also possible to construct a reactive SessionFactory via programmatic
configuration based on Hibernate’s ServiceRegistry architecture, by using a
ReactiveServiceRegistryBuilder . But that’s outside the scope of this document.
|
1.3.9. Obtaining a reactive session
Persistence operations are exposed via a reactive Session
object. It’s very
important to understand that most operations of this interface are non-blocking,
and execution of SQL against the database is never performed synchronously.
Persistence operations that belong to a single unit of work must be chained by
composition within a single reactive stream.
Also remember that a Hibernate session is a lightweight object that should be created, used, and then discarded within a single logical unit of work.
| That is to say, you should reuse the same session across multiple persistence operations within a single reactive stream representing a certain transaction or unit of work, but don’t share a session between different concurrent reactive streams! |
To obtain a reactive Session
from the SessionFactory
, use withSession()
:
sessionFactory.withSession(
session -> session.find(Book.class, id)
.invoke(
book -> ... //do something with the book
)
);
The resulting Session
object is automatically associated with the current
reactive stream, and so nested calls to withSession()
in a given stream
automatically obtain the same shared session.
Alternatively, you may use openSession()
, but you must remember to close()
the session when you’re done.
And you must take great care to only access each session from within exactly
one Vert.x context. (See Sessions and Vert.x contexts more on this).
Uni<Session> sessionUni = sessionFactory.openSession();
sessionUni.chain(
session -> session.find(Book.class, id)
.invoke(
book -> ... //do something with the book
)
.eventually(session::close)
);
1.3.10. Using the reactive session
The Session
interface has methods with the same names as methods of the JPA
EntityManager
. You might already be familiar with the following session
operations defined by JPA:
| Method name and parameters | Effect |
|---|---|
|
Obtain a persistent object given its type and its id (primary key) |
|
Make a transient object persistent and schedule a SQL
|
|
Make a persistent object transient and schedule a SQL
|
|
Copy the state of a given detached object to a corresponding managed persistent instance and return the persistent object |
|
Refresh the persistent state of an object using a new
SQL |
|
Obtain a pessimistic lock on a persistent object |
|
Detect changes made to persistent objects association
with the session and synchronize the database state
with the state of the session by executing SQL |
|
Disassociate a persistent object from a session without affecting the database |
|
Obtain a reference to a persistent object without actually loading its state from the database |
If you’re not familiar with these operations, don’t despair! Their semantics are defined in the JPA specification, and in the API documentation, and are explained in innumerable articles and blog posts. But if you already have some experience with Hibernate or JPA, you’re right at home!
| Just like in Hibernate ORM, the session is considered to be unusable after any of its methods throws an exception. If you receive an exception from Hibernate Reactive, you should immediately close and discard the current session. |
Now, here’s where Hibernate Reactive is different: in the reactive API, each
of these methods returns its result in a non-blocking fashion via a Java
CompletionStage
(or Mutiny Uni
). For example:
session.find(Book.class, book.id)
.invoke( book -> System.out.println(book.title + " is a great book!") )
On the other hand, methods with no meaningful return value just return
CompletionStage<Void>
(or Uni<Void>
).
session.find(Book.class, id)
.call( book -> session.remove(book) )
.call( () -> session.flush() )
| The session will be flushed automatically at the end of a unit of work if—and only if—you use a transaction, as described below in Transactions. If you don’t use a transaction, and forget to flush the session explicitly, your persistence operations might never be sent to the database! |
An extremely common mistake when using reactive streams is to forget to
chain the return value of a "void-like" method. For example, in the following
code, the flush()
operation is never executed, because invoke()
doesn’t
chain its return value to the tip of the stream.
session.find(Book.class, id)
.call( book -> session.remove(book) )
.invoke( () -> session.flush() ) //OOPS, WRONG!!
So remember:
-
You must use
thenCompose()
, notthenAccept()
, when calling "void-like" methods that returnCompletionStage
. -
In Mutiny, you must use
call()
, notinvoke()
, when calling "void-like" methods that returnUni
.
The same problem occurs in the following code, but this time it’s remove()
that never gets called:
session.find(Book.class, id)
.call( book -> {
session.remove(book); //OOPS, WRONG!!
return session.flush();
} )
If you already have some experience with reactive programming, there’s nothing new to learn here. But if you are new to reactive programming, just be aware that you’re going to make this mistake, in some form, at least once!
1.3.11. Queries
Naturally, the Session
interface is a factory for Query
instances which
allow you to set query parameters and execute queries and DML statements:
| Method name | Effect |
|---|---|
|
Obtain a |
|
Obtain a |
|
Obtain a |
That createQuery()
method produces a reactive Query
, allowing HQL / JPQL
queries to be executed asynchronously, always returning their results via a
CompletionStage
(or Uni
):
session.createQuery("select title from Book order by title desc")
.getResultList()
.invoke( list -> list.forEach(System.out::println) )
The Query
interface defines the following important operations:
| Method name | Effect |
|---|---|
|
Set an argument of a query parameter |
|
Limit the number of results returned by the query |
|
Specify a certain number of initial results to be skipped (for result pagination) |
|
Execute a query and obtain the single result |
|
Execute a query and obtain the results as a list |
|
Execute a DML statement and obtain the number of affected rows |
The Hibernate Reactive Query API doesn’t support java.util.Date
or its subclasses in java.sql , nor java.util.Calendar . Always use
java.time types like LocalDate or LocalDateTime for specifying
arguments to temporally-typed query parameters.
|
For JPA criteria queries, you must first obtain the CriteriaBuilder
using
SessionFactory.getCriteriaBuilder()
, and execute your query using
Session.createQuery()
.
CriteriaQuery<Book> query = factory.getCriteriaBuilder().createQuery(Book.class);
Root<Author> a = query.from(Author.class);
Join<Author,Book> b = a.join(Author_.books);
query.where( a.get(Author_.name).in("Neal Stephenson", "William Gibson") );
query.select(b);
return session.createQuery(query).getResultList().invoke(
books -> books.forEach( book -> out.println(book.title) )
);
1.3.12.  In Hibernate Reactive, on
the other hand, lazy association fetching is an asynchronous process that
produces a result via a CompletionStage
(or Uni
).
Therefore, lazy fetching is an explicit operation named fetch()
, a static
method of Stage
and Mutiny
:
session.find(Author.class, author.id)
.chain( author -> Mutiny.fetch(author.books) )
.invoke( books -> ... )
Of course, this isn’t necessary if you fetch the association eagerly.
| It’s very important to make sure you’ve fetched all the data that will be needed before passing control to the process that renders the UI! There is no transparent lazy fetching in Hibernate Reactive, so patterns like "open session in view" will not help at all. |
Sometimes you might need to chain multiple calls to fetch()
, for
example:
Mutiny.fetch( session.getReference(detachedAuthor) )
.chain( author -> Mutiny.fetch(author.books) )
.invoke( books -> ... )
fetch() isn’t recursive! You can’t fetch an association
belonging to an unfetched entity without fetching the entity instance
first.
|
1.3.13. Field-level lazy fetching
Similarly, field-level lazy fetching—an advanced feature, which is only supported in conjunction with Hibernate’s optional compile-time bytecode enhancer—is also an explicit operation:
session.find(Book.class, book.id)
.chain( book -> session.fetch(book, Book_.isbn) )
.invoke( isbn -> ... )
Note that the field to fetch is identified by a JPA metamodel Attribute
.
| We don’t encourage you to use field-level lazy fetching unless you have very specific requirements. |
1.3.14. Transactions
The withTransaction()
method performs work within the scope of a database
transaction.
session.withTransaction( tx -> session.persist(book) )
The session is automatically flushed at the end of the transaction.
For a given Session
object, nested calls to withTransaction()
occur
within the same shared transaction context. However, notice that the
transaction is a resource local transaction only, delegated to the
underlying Vert.x database client, and does not span multiple datasources,
nor integrate with JPA container-managed transactions.
| Hibernate Reactive does not currently support distributed (XA) transactions. |
For extra convenience, there’s a method that opens a session and starts a transaction in one call:
sessionFactory.withTransaction( (session, tx) -> session.persist(book) )
This is probably the most convenient thing to use most of the time.
1.4. Integrating with Vert.x
At runtime, interaction with the database happens on a Vert.x thread, typically the event loop thread. When you write code that creates and destroys Hibernate Reactive sessions, it’s important to understand how sessions relate to threads and Vert.x contexts.
1.4.1. Sessions and Vert.x contexts
| Remember how in regular old Hibernate JPA, you’re not supposed to share a session between multiple threads? Well, the idea here is essentially similar, it’s just that the notion of a "thread" is a little more slippery, or at least more technical. You need to be able to replace the idea of a "thread" with the idea of a chain of callbacks occurring on a reactive stream, all within the scope of a certain Vert.x local context. |
When you create a session using withSession()
or withTransaction()
, it’s
automatically associated with the current Vert.x
local context, and propagates with the local context,
as mentioned above in Obtaining a reactive session. And you’re only
allowed to use the session from the thread that owns this local context. If
you screw up, and use it from a different thread, you might see this error:
HR000068: This method should exclusively be invoked from a Vert.x EventLoop thread; ...
On the other hand, if you use openSession()
, you’ll have to manage the
association between sessions and contexts yourself. Now, that’s in principle
straightforward, but you’d be surprised how often people mess up.
| The session is not thread-safe (or "stream-safe"), so using it across different threads (or reactive streams) may cause bugs that are extremely hard to detect. Don’t say we didn’t warn you! |
For example, I bet you would like to be able to write code like this:
List<CompletionStage> list = ...
for (Entity entity : entities) {
list.add(session.persist(entity));
}
CompletableFuture.allOf(list).thenCompose(session::flush);
Well, we’re sorry, but that’s just not allowed. Parallel reactive streams may not share a session. Each stream must have its own session.
1.4.2. Executing code in a Vert.x context
What if you need to run a block of code within the scope of a Vert.x context, but
the current thread isn’t associated with a Context
? One solution is to obtain a
Vert.x Context
object using getOrCreateContext()
and
then call runOnContext()
to execute the code in that
context.
Context currentContext = Vertx.currentContext();
currentContext.runOnContext( event -> {
// Here you will be able to use the session
});
Within the block of code passed to runOnContext()
, you’ll be able to use the
Hibernate Reactive session associated with the context.
1.4.3. Vert.x instance service
The VertxInstance
service defines how Hibernate Reactive obtains an instance
of Vert.x. The default implementation just creates one the first time it’s
needed. But if your program requires control over how the Vert.x instance is
created, or how it’s obtained, you can override the default implementation and
provide your own VertxInstance
. Let’s consider this example:
public class MyVertx implements VertxInstance {
private final Vertx vertx;
public MyVertx() {
this.vertx = Vertx.vertx();
}
@Override
public Vertx getVertx() {
return vertx;
}
}
One way to register this implementation is to configure Hibernate programmatically, for example:
Configuration configuration = new Configuration();
StandardServiceRegistryBuilder builder = new ReactiveServiceRegistryBuilder()
.addService( VertxInstance.class, new MyVertx() )
.applySettings( configuration.getProperties() );
StandardServiceRegistry registry = builder.build();
SessionFactory sessionFactory = configuration.buildSessionFactory( registry );
Alternatively, you could implement the ServiceContributor
interface.
public class MyServiceContributor implements ServiceContributor {
@Override
public void contribute(StandardServiceRegistryBuilder serviceRegistryBuilder) {
serviceRegistryBuilder.addService( VertxInstance.class, new MyVertxProvider() );
}
}
To register this ServiceContributor
, add a text file named
org.hibernate.service.spi.ServiceContributor
to /META-INF/services/
.
org.myproject.MyServiceContributor
1.5. Tuning and performance
Once you have a program up and running using Hibernate Reactive to access the database, it’s inevitable that you’ll find places where performance is disappointing or unacceptable.
Fortunately, most performance problems are relatively easy to solve with the tools that Hibernate makes available to you, as long as you keep a couple of simple principles in mind.
First and most important: the reason you’re using Hibernate Reactive is because it makes things easier. If, for a certain problem, it’s making things harder, stop using it. Solve this problem with a different tool instead.
| Just because you’re using Hibernate in your program doesn’t mean you have to use it everywhere. |
Second: there are two main potential sources of performance bottlenecks in a program that uses Hibernate:
-
too many round trips to the database, and
-
memory consumption associated with the first-level (session) cache.
So performance tuning primarily involves reducing the number of accesses to the database, and/or controlling the size of the session cache.
But before we get to those more advanced topics, we should start by tuning the connection pool.
1.5.1. Tuning the Vert.x pool
In Basic configuration we already saw how to set the size of the Vert.x database connection pool. When it comes time for performance tuning, you can further customize the pool and prepared statement cache via the following configuration properties:
| Configuration property name | Purpose |
|---|---|
|
The maximum connection requests allowed in the wait queue |
|
The maximum time to wait when requesting a pooled connection, in milliseconds |
|
The maximum time a connection may sit idle, in milliseconds |
|
The Vert.x connection pool cleaner period, in milliseconds |
|
The maximum size of the prepared statement cache |
|
The maximum length of prepared statement SQL string that will be cached |
Finally, for more advanced cases, you can write your own code to configure
the Vert.x client by implementing SqlClientPoolConfiguration
.
| Configuration property name | Purpose |
|---|---|
|
A class implementing |
1.5.2. Enabling statement batching
An easy way to improve performance of some transactions with almost no work at all is to turn on automatic DML statement batching. Batching only helps in cases where a program executes many inserts, updates, or deletes against the same table in a single transaction.
All you need to do is set a single property:
| Configuration property name | Purpose |
|---|---|
|
Maximum batch size for SQL statement batching |
(Again, this property has jdbc
in its name, but Hibernate Reactive
repurposes it for use with the reactive connection.)
Even better than DML statement batching is the use of HQL update
or delete queries, or even native SQL that calls a stored procedure!
|
1.5.3. Association fetching
Achieving high performance in ORM means minimizing the number of round trips to the database. This goal should be uppermost in your mind whenever you’re writing data access code with Hibernate. The most fundamental rule of thumb in ORM is:
-
explicitly specify all the data you’re going to need right at the start of a session/transaction, and fetch it immediately in one or two queries,
-
and only then start navigating associations between persistent entities.
Without question, the most common cause of poorly-performing data access code in Java programs is the problem of N+1 selects. Here, a list of N rows is retrieved from the database in an initial query, and then associated instances of a related entity are fetched using N subsequent queries.
| Hibernate code which does this is bad code and makes Hibernate look bad to people who don’t realize that it’s their own fault for not following the advice in this section! |
Hibernate provides several strategies for efficiently fetching associations and avoiding N+1 selects:
-
outer join fetching,
-
batch fetching, and
-
subselect fetching.
Of these, you should almost always use outer join fetching. Batch fetching and subselect fetching are only useful in rare cases where outer join fetching would result in a cartesian product and a huge result set. Unfortunately, outer join fetching simply isn’t possible with lazy fetching.
| Avoid the use of lazy fetching, which is often the source of N+1 selects. |
It follows from this tip that you shouldn’t need to use Stage.fetch()
or Mutiny.fetch()
very often!
Now, we’re not saying that associations should be mapped for eager fetching by default! That would be a terrible idea, resulting in simple session operations that fetch the entire database! Therefore:
| Most associations should be mapped for lazy fetching by default. |
It sounds as if this tip is in contradiction to the previous one, but it’s not. It’s saying that you must explicitly specify eager fetching for associations precisely when and where they are needed.
If you need eager fetching in some particular transaction, use:
-
left join fetch
in HQL, -
a fetch profile,
-
a JPA
EntityGraph
, or -
fetch()
in a criteria query.
You can find much more information about association fetching in the documentation for Hibernate ORM.
1.5.4. Enabling the second-level cache
A classic way to reduce the number of accesses to the database is to use a second-level cache, allowing cached data to be shared between sessions.
Hibernate Reactive supports second-level cache implementations that perform no blocking I/O.
| Make sure you disable any disk-based storage or distributed replication used by your preferred cache implementation. A second-level cache which uses blocking I/O to interact with the network or disk-based storage will at least partially negate the advantages of the reactive programming model. |
Configuring Hibernate’s second-level cache is a rather involved topic, and quite outside the scope of this document. But in case it helps, we’re testing Hibernate Reactive with the following configuration, which uses EHCache as the cache implementation, as above in Optional dependencies:
| Configuration property name | Property value |
|---|---|
|
|
|
|
|
|
|
|
If you’re using EHCache, you’ll also need to include an ehcache.xml
file
that explicitly configures the behavior of each cache region belonging to
your entities and collections.
Don’t forget that you need to explicitly mark each entity that will
be stored in the second-level cache with the @Cache annotation from
org.hibernate.annotations .
|
You can find much more information about the second-level cache in the documentation for Hibernate ORM.
1.5.5. Session cache management
Entity instances aren’t automatically evicted from the session cache when they’re no longer needed. (The session cache is quite different to the second-level cache in this respect!) Instead, they stay pinned in memory until the session they belong to is discarded by your program.
The methods detach()
and clear()
allow you to remove entities from the
session cache, making them available for garbage collection. Since most
sessions are rather short-lived, you won’t need these operations very often.
And if you find yourself thinking you do need them in a certain situation,
you should strongly consider an alternative solution: a stateless session.
1.5.6. Stateless sessions
An arguably-underappreciated feature of Hibernate is the StatelessSession
interface, which provides a command-oriented, more bare-metal approach to
interacting with the database.
You may obtain a reactive stateless session from the SessionFactory
:
Stage.StatelessSession ss = getSessionFactory().openStatelessSession();
A stateless session:
-
doesn’t have a first-level cache (persistence context), nor does it interact with any second-level caches, and
-
doesn’t implement transactional write-behind or automatic dirty checking, so all operations are executed immediately when they’re explicitly called.
For a stateless session, you’re always working with detached objects. Thus, the programming model is a bit different:
| Method name and parameters | Effect |
|---|---|
|
Obtain a detached object, given its type and its id,
by executing a |
|
Fetch an association of a detached object |
|
Refresh the state of a detached object by executing
a |
|
Immediately |
|
Immediately |
|
Immediately |
There’s no flush() operation, and so update() is always explicit.
|
In certain circumstances, this makes stateless sessions easier to work with, but with the caveat that a stateless session is much more vulnerable to data aliasing effects, since it’s easy to get two non-identical Java objects which both represent the same row of a database table.
If you use fetch() in a stateless session, you can very easily
obtain two objects representing the same database row!
|
In particular, the absence of a persistence context means that you can safely
perform bulk-processing tasks without allocating huge quantities of memory.
Use of a StatelessSession
alleviates the need to call:
-
clear()
ordetach()
to perform first-level cache management, and -
setCacheMode()
to bypass interaction with the second-level cache.
| Stateless sessions can be useful, but for bulk operations on huge datasets, Hibernate can’t possibly compete with stored procedures! |
When using a stateless session, you should be aware of the following additional limitations:
-
persistence operations never cascade to associated instances,
-
changes to
@ManyToMany
associations and@ElementCollection
s cannot be made persistent, and -
operations performed via a stateless session bypass callbacks.
1.5.7. Optimistic and pessimistic locking
Finally, an aspect of behavior under load that we didn’t mention above is row-level data contention. When many transactions try to read and update the same data, the program might become unresponsive with lock escalation, deadlocks, and lock acquisition timeout errors.
There’s two basic approaches to data concurrency in Hibernate:
-
optimistic locking using
@Version
columns, and -
database-level pessimistic locking using the SQL
for update
syntax (or equivalent).
In the Hibernate community it’s much more common to use optimistic locking, and Hibernate makes that incredibly easy.
| Where possible, in a multiuser system, avoid holding a pessimistic lock across a user interaction. Indeed, the usual practice is to avoid having transactions that span user interactions. For multiuser systems, optimistic locking is king. |
That said, there is also a place for pessimistic locks, which can sometimes reduce the probability of transaction rollbacks.
Therefore, the find()
, lock()
, and refresh()
methods of the reactive session
accept an optional LockMode
. You can also specify a LockMode
for a query. The
lock mode can be used to request a pessimistic lock, or to customize the behavior
of optimistic locking:
LockMode type |
Meaning |
|---|---|
|
An optimistic lock obtained implicitly whenever
an entity is read from the database using |
|
An optimistic lock obtained when an entity is
read from the database, and verified using a
|
|
An optimistic lock obtained when an entity is
read from the database, and enforced using an
|
|
A pessimistic lock obtained implicitly whenever
an entity is written to the database using
|
|
A pessimistic |
|
A pessimistic |
|
A pessimistic lock enforced using an immediate
|
1.6. Custom connection management and multitenancy
Hibernate Reactive supports custom management of reactive connections by letting
you define your own implementation of ReactiveConnectionPool
, or extend the
built-in implementation DefaultSqlClientPool
.
| Configuration property name | Value |
|---|---|
|
A class which implements |
A common motivation for defining a custom pool is the need to support multitenancy.
In a multitenant application, the database or database schema depends on the current
tenant identifier. The easiest way to set this up in Hibernate Reactive is to extend
DefaultSqlClientPool
and override getTenantPool(String tenantId)
.
For multitenancy, you’ll also need to set at least one of the following configuration properties defined by Hibernate ORM:
| Configuration property name | Value |
|---|---|
|
The multitenancy strategy: |
|
(Optional) A class which implements |
If you don’t provide a CurrentTenantIdentifierResolver
, you can specify
the tenant id explicitly when you call openSession()
, withSession()
,
or withTransaction()
.


class msiempy.ESM(NitroObject): (source)
Enterprise Security Manager interface.
Object do not contain data, it's a simple interface to data structures / values returned by the SIEM and other helper methods.
Fetch a few ESM infos
>>> from msiempy import ESM >>> esm=ESM() >>> esm.version() '11.2.1' >>> esm.recs() [('ERC-1',<PHONE_NUMBER>87483648)] >>> esm.buildstamp() '11.2.1<PHONE_NUMBER>0014'
| Method | __init__ | Undocumented |
| Class Variable | text | String representation: "ESM version <YOUR_ESM_VERSION>" |
| Class Variable | json | Not implemented |
| Method | refresh | Not implemented |
| Method | time | The current ESM time. |
| Method | buildstamp | ESM Buildstamp |
| Method | version | ESM version |
| Method | status | Get various ESM statuses. |
| Method | disks | |
| Method | ram | |
| Method | backup_status | |
| Method | callhome | Check callhome connection. |
| Method | rules_status | Returns: Rules autocheck status and timestamps. |
| Method | get_alerts | Tells the ESM to retrieve alerts from the provided device ID. |
| Method | recs | Get list of receivers name and id |
| Method | tz_offsets | Builds table of ESM timezones including offsets. |
| Method | timezones | Builds table of ESM timezones and names only. No offsets. |
| Method | tz_name_to_id | Get the timezone ID from it's name. |
| Method | tz_id_to_name | Get the timezone name from it's ID. |
| Method | type_id_to_venmod | Get the datasource vendor info from it's type_id . |
| Method | venmod_to_type_id | Get the datasource type_id info from it's vendor and model . |
| Method | rules_history | |
| Method | _get_text | Abstract declaration. |
| Method | _get_json | Abstract declaration. |
| Method | _map_status_int_fields | Undocumented |
| Instance Variable | _fields | Undocumented |
| Method | _get_timezones | Gets list of timezones from the ESM. |
| Method | _get_ds_types | Retrieves device table from ESM |
| Method | _format_ds_types | Callback to create type_id/vendor/model table |
Inherited from NitroObject:
| Class | NitroJSONEncoder | Custom JSON encoder that will use the approprtiate propertie depending of the type of NitroObject. TODO support json json dumping of QueryFilers, may be by making them inherits from NitroDict. |
| Instance Variable | nitro | Reference to the NitroSession , interface to the SIEM. |
Undocumented
| Returns | str: ESM time (GMT). i.e. "2017-07-06T12:21:59.0+0000" |
Get various ESM statuses.
Avail: 7977MB, Used: 7857MB, Free: 119MB
sda3 Size: 491GB, Used: 55GB(12%), Available: 413GB, Mount: /
Other functions exist to return subsets of this data also.
| Returns | dict | |
| Note | Uses internal API method SYS_GETSYSINFO |
Example:
{'autoBackupEnabled': True, 'autoBackupDay': 7, 'autoBackupHour': 0, 'backupLastTime': '07/03/2017 08:59:36', 'backupNextTime': '07/10/2017 08:59'}
| Returns | dict : Backup status and timestamps. |
| Returns | bool : If there is currently a callhome connection. |
Returns: Rules autocheck status and timestamps.
Example:
{ 'rulesAndSoftwareCheckEnabled': True 'rulesAndSoftLastCheck': '07/06/2017 10:28:43', 'rulesAndSoftNextCheck': '07/06/2017 22:28:43',}
| Returns | list[tuple(name, id)] |
Builds table of ESM timezones including offsets.
Example:
[(1, 'Midway Island, Samoa', '-11:00'), (2, 'Hawaii', '-10:00'), ... ]
| Returns | list[tuple()] : list of timezone tuples(name, id, offset) |
| Returns | dict : {timezone_id:timezone_name, ...} |
| Returns | list of tuples output from callback: _format_ds_types() |
| Parameters | venmods | (obj ): request object from _get_ds_types |
| Returns |
[(542, 'McAfee', 'SaaS Email Protection') (326, 'McAfee', 'Web Gateway') (406, 'Microsoft', 'ACS - SQL Pull') (491, 'Microsoft', 'Endpoint Protection - SQL Pull') (348, 'Microsoft', 'Exchange')] | |
| Note | This is a callback for _get_ds_types. |


{% from "include/_formhelpers.html" import render_field %} {% extends "layout/base.html" %} {% include "layout/nav.html" %} {% block head %}


I have 4+ plus years of experience in providing information about the quality of product under
development, regardless of where in development that product is.
I read specs, create test cases, validate api, write scripts to automate, analyze performance
and occasionally perform security audit for the product.
Certified ISTQB Software Tester Foundation Level (CTFL) | Flutter Enthusiast


On the Horizon
The weather of the future.
☰
Home
Franklin
Greenville
Springfield
Storm Center
Gallery


Farmer arrested, 15 others fined in UP's Shamli for stubble burning
During the checking, 15 farmers in Shamli's Kairana tehsil were fined Rs 2,500 each for stubble burning
by Press Trust of IndiaA farmer was arrested and 15 others were fined for burning crop residue in their fields in Uttar Pradesh's Shamli district, officials said on Thursday.
The action was taken during a spot check on Wednesday, they said.
Tara Chand was arrested after he was found burning crop residue in his fields in Margub Garh village under Thanabhawan police station, Sub-Divisional Magistrate Sandeep Kumar said.
A case has been registered against him under section 278 (making atmosphere noxious to health) of the Indian Penal Code, he said.
During the checking, 15 farmers in Shamli's Kairana tehsil were fined Rs 2,500 each for stubble burning, Kumar said.
In the neighbouring Muzaffarnagar district, a farmer, Sunil Kumar, was booked for stubble burning on Wednesday.
He was booked following a complaint lodged by a revenue official, Sub-Divisional Magistrate Vijay Kumar said.
Delhi and the National Capital Region (NCR) have been grappling with hazardous levels of air pollution since late October. Stubble burning by farmers in the neighbouring states contributes to the pollution in the national capital.


2012
Liquid error: No such file or directory - /home/jhjguxin/jhjguxin.github.com/_includes
Liquid error: No such file or directory - /home/jhjguxin/jhjguxin.github.com/_includes
Liquid error: No such file or directory - /home/jhjguxin/jhjguxin.github.com/_includes


{% define "title" %}Hello, World!{% end %} {% define "body" %}
This is a skeleton application that has been automatically generated by Goal Toolkit.


Connecting your domain by using Google Firebase
Google Chrome announced that they are going to show “Not secure” message on their address bar from this October, if Chrome trying to enter URL with ‘HTTP’. Most of the major websites, such as Google, Twitter, Facebook — were already move their protocol to HTTPS.
However, with Firebase, you can get your own SSL certificate(for free!), and you don’t need to worry about ‘Not secure’ message even if you don’t have your own SSL certificate right now. With your Google account, you can create a Firebase project at Google Firebase.
Create a new project
Dialog shows up after you click “Add project” button. You can name your new project and select your country here. I selected “South Korea”, But I recommend you to select the country which has the best CDN server located(and of course, the nearest country!)
Start Google Firebase
You must setup your local directory first, because it’s based on Commend Line Interface of Node.js, not like a FTP server or Web hosting manager. Just make sure you installed Node.js on your computer!
Connect your domain
After this, click the “Connect domain” button to start connecting your domain to Firebase. In this step, you need your own domain, or domain to redirection.
In my case, my DNS provider is “GoDaddy”. So I added the TXT record on my DNS provider’s DNS setting.
Upload your files
Next thing to do is bringing your assets into your directory, which you’ve setup on terminal. In my case, I set my public directory as “public” folder(default). And then, on your Terminal, type a line of commend below.
Get 'https'
That’s it! Now you have to wait for 5 mins to 2 hours for Firebase to applying your domain’s settings. Before that, uncomfortable screen may come across to you — when you’re trying to enter your domain, like
I did this for my design portfolio website, cause I don’t want
to make my visitors get scared by “Not Secure” message on
Chrome.(seriously...!)
And after 30 mins...
Hope this can help you, especially for people who are
‘non-professional’ in programming fields like me. Thanks!
Title : Connecting your domain by using Google
Firebase(w/ Node.js)
Date : May 19, 2017
Writer : Hyouk Seo (Spemer)


Posted on January 16, 2016
What are the differences between margin, border, and padding?
Margin refers to the space between tag elements. This space would be similar to the casing and screen protector on your phone; you use a case to protect the phone from other elements, but still consider it part of the phone when you go and pick it up.
Border refers to the space that goes around the content and padding. In the above analogy, it would be the phone.
Padding refers to the space around the content; that is the information of interest. Using the phone analogy, this would mean the motherboard and other peripherals that make the connections for the phone. The content would then be the software that runs the phone.
To illustrate this, take a look at the image below that shows the layers of the box model. Another way of thinking about this is like an onion; each layer can be different in thickness and it separates one layer from another.


date
Syntax
date (var d : string)
Description
The date statement is used to determine the current date. Variable d is assigned a string in the format "dd mmm yy", where mmm is the first 3 characters of the month, e.g., "Apr". For example, if the date is Christmas 2002, d will be set to "25 Dec 02".
Example
This program greets you and tells you the date.
var today : string
date (today)
put "Greetings!! The date today is ", today
Details
Be warned that Turing gets the date from the operating system. If the date is is not correctly set in the operating system, then the date procedure will give incorrect results.
See also
delay, clock, sysclock, wallclock and time statements.
See also predefined unit Time.


Saturday, August 8, 2009
8 am–6 pm
The Crossings
About usConscious Fertility Cooperative was founded by a group of healthcare professionals passionately committed to assisting women on their fertility journey. Their goal is to create a supportive network and an educational venue that inspires and empowers women throughout all phases of their reproductive health. What will I gain from the retreat?The retreat is designed to create a comfortable space for women undergoing fertility challenges. You will learn self-care techniques from both Western and Eastern philosophies. Topics will include understanding your body’s natural rhythms, nutrition and lifestyle, the biomechanical affects of stress, and how to acknowledge your body’s intuition. Included is a group acupuncture session. |
Our mission statementTo cultivate optimal fertility and reproductive health through an integrated network of healthcare professionals. PricingThe full day retreat cost, including breakfast and lunch, is $375. To ensure an intimate setting, space is limited to 15 women. ReservationsTo reserve your spot, or for more information, contact us at<PHONE_NUMBER> or visit us at www.fertilityco-op.com. |
Sarai Stapleton Maom, L.Ac., FabormSarai, a licensed Dipl. of Oriental Medicine, owns Axis Acupuncture of Austin and specializes in fertility challenges and reproductive health. She is an inaugural fellow of the American Board of Oriental Reproductive Medicine, and began her practice initially with Randine Lewis, founder of The Fertile Soul and author of The Infertility Cure. Sarai created CFC as a means to empower women in their journey to conceive by providing resources that are educated in modern medicine and steeped in ancient tradition to help optimize fertility. |
ROSALIND HANEY, RN, CCNRosalind provides fertility guidance through her knowledge as a Registered Nurse, clinical nutritionist, childbirth educator and infertility nurse. She believes strongly in the body’s inherent ability to heal and reproduce. Rosalind teaches the biology of reproductive health and helps individuals and couples identify the simple diet and lifestyle choices they need to enable fertility and a healthy pregnancy. |
SANDY GARCIA, MDDr. Garcia has been a pediatric ER physician at Dell Children’s hospital for 10 years. She is an Austin native, with interests in complementary medicine and fertility. She brings experiential as well as western medical knowledge to benefit those going through the fertility journey. |
DAPHNE CARRICK, RN, MSNDaphne has been a registered nurse for 19 years and holds a Masters degree in Nursing. Licensed as a Clinical Nurse Specialist in California, she spent the majority of her nursing career in Women’s Health.  Due to experiencing her own fertility challenges, her focus and passion shifted to supporting and empowering other women who are experiencing similar difficulties. |


Go to homepage


WITNESS LIST Corrections Committee February 27, 2001-2:00P HB 123 Against: Reeves, Linda (Inmate Families Organization, Inc.) On: Johnson, Gary (Texas Department of Criminal Justice) Morrow, Mike (Windham School District) HB 124 Against: Martin Roundtree, Meredith (Texas Criminal Defense Lawyers Association) Reeves, Linda (Inmate Familes Organization, Inc) On: Johnson, Gary (Texas Department of Criminal Justice) Registering, but not testifying: Against: Harrell, Will (ACLU of Texas) HB 588 For: Harrell, Will (ACLU of Texas) On: Johnson, Pat (Department of Public Safety) Registering, but not testifying: For: Lawrence, Kevin F. (Texas Municipal Police Association) HB 773 Registering, but not testifying: On: Hightower, Allen (Correctional Managed Health Care) Keith, Matthew R. (UTMB Correctional Managed Health Care) Mendez-Lewis, Maria (Comptroller of Public Accounts) Sapp, Allen (Correctional Managed Health Care Committee) HB 1429 For: Hubbarth, William "Rusty" (Self and Justice for All) Kahan, Andy (Mayor's Crime Victims Office) Ketchum, Ricky (Self) LeBleu, Dorothy N. (Self) Pitrucha, Madonna (Self) Strong, Wendy (Self and Justice For All) Teer, Pat (Self) Against: Cohen, Gary (Texas Criminal Defense Lawyers Association) Reeves, Linda (Inmate Families Organization, Inc.) On: Garrett, Gerald (Texas Board of Pardons and Paroles)


MD5PASS(1) Syslinux Project MD5PASS(1) NAME md5pass - Create an MD5 password hash SYNOPSIS md5pass [PASSWORD] [SALT] DESCRIPTION md5pass creates an MD5 password hash. In the absence of a salt value on the command line, a random salt vector will be generated. SEE ALSO syslinux(1) HOMEPAGE More information about the Syslinux project can be found on the homepage at <http://syslinux.zytor.com/>. AUTHOR md5pass was written by H. Peter Anvin<EMAIL_ADDRESS>This manual page was written by Daniel Baumann <daniel.baumann@progress- technologies.net>. 4.00 2010-06-22 MD5PASS(1)


super().__init__(name, seed, **kwargs)
name = _get_method_by_alias(name, "ss")
self.name = name
self.state = np.random.RandomState(seed=seed)
self.distr = getattr(ss, self.name)(**kwargs)
def sample(self, size):
Sampling method of ``ScipySampler``.
self.right = right
// calculate probs of samplers in mixture
_ws = np.array([self.left.weight, self.right.weight])
self.weight = np.sum(_ws)
self._normed = _ws / np.sum(_ws)
def sample(self, size):


Triad Semiconductor – Automotive ASIC Design Experience
- Child-proof door lock control IC
- Anti-pinch Window lifter IC
- Vehicle Climate control IC
- Airbag, buckle detector, headlamp/foglamp driver IC
- Transmission fluid pressure sensor IC
- Seat Positioning motor IC
- Ultrasonic Parking Assistance IC
- Vehicle Stability sensor IC
- Automotive Compass IC
Mixed Signal IP for Automotive ASIC Applications
- Smart USB Charger
- Automotive Wireless Inductive Charger
- Fuel Injector Drivers
- Motor Controllers
- Power Management and Line drivers for Rear View Camera
- Suspension Controls
- Mass Air Flow Sensor Electronics
- Temperature Sensor Electronics
- Oxygen Sensor Electronics
- Force Sensor Electronics
- Strain Gage Sensor Electronics
- Heads Up Display Driver
- CAN and Lin PHYs and Controllers
- Multiphase Boost Converter
- Driver Tracking System
- Traction Controls
- Adaptive Suspension
- Transmission Controller
- Capacitive Sense Door Handles
- LED Drivers
- Unique requirements for Automotive ASICs
- Load Dump


{% extends "proposal/form.html" %} {% load get_prof %} {% block title %} Email Primary Investigator Form — Office of Sponsored Programs — Carthage College {% endblock %} {% block content %}
Compose an email to send to: {{p.user.first_name}} {{p.user.last_name}} ({{p.user.email}})
If you are satisfied with the content below, click on the "execute" button, and your email will be sent.
If you would like to update the content, click on the "edit" button.


Love to share, travelling, meet new people, enthusiastic about technology especially web technologies and islamic knowledge.
As a Backend Developer, I responsible to develop rest API.
I responsible to develop and maintain the web application.
As a fullstack, I responsible to develop and maintain the web application.
I responsible to develop and maintain the web application.
Self-employed for various startup company based in Cianjur-Bandung.
I am responsible for providing learning to students at school
Enjoying share, talk and discuss with other developers, joining in comunnity and also attending conferences, try something new in the programming.


@()
There is no control, no restriction, you can vote as many times as you want, but we trust you! So please, don't cheat too much.
http://devoxx-pdi.rhcloud.com
https://github.com/pauldijou/devoxx


Gateway
BOT
22-Aug-18 07:27 PM
[dscrdeql] <KQweci-Mod> btw I am getting on the rich list this weekend
[dscrdeql] <SwimedDragon> Isn't too hard to get on there is it?
[dscrdeql] <SwimedDragon> Or you mean the very top?
[dscrdeql] <KQweci-Mod> Nope. I am buying some more Eqli from exrates this weekend
[dscrdeql] <KQweci-Mod> Getting on the rich list means buy cheap and hodl for high profit
[dscrdeql] <SwimedDragon> How much is needed for the rich list?
[dscrdeql] <KQweci-Mod> There is nothing like a rich list on here. I am talking in general. I am getting more Eqli from exrates
[dscrdeql] <KQweci-Mod> I hope you understand
[dscrdeql] <SwimedDragon> Oh I thought the top 100 was the rich list
[dscrdeql] <SwimedDragon> Or is that different things?
[dscrdeql] <SwimedDragon> But I think I understand what you mean now
[dscrdeql] <KQweci-Mod> I think very soon we will be begging people to dump for us to buy cheap
[dscrdeql] <SwimedDragon> Yeah
[dscrdeql] <Jellysmash> haha...that top 100 'richlist' is just a list of addresses
[dscrdeql] <Jellysmash> with the most EQLI in it
[dscrdeql] <SwimedDragon> It's even insane that prices are at where they are now
[dscrdeql] <Jellysmash> yes, if you buy some eqli you can get on the rich list
[dscrdeql] <SwimedDragon> I've seen other people call it richlist so I thought it was just that
[dscrdeql] <Jellysmash> you don't need that much
[dscrdeql] <Jellysmash> i'm in there
[dscrdeql] <Jellysmash> and i know a few others as well
[dscrdeql] <Jellysmash> come join us
[dscrdeql] <Jellysmash> go to exrates and buy
[dscrdeql] <SwimedDragon> Half the discord is probably on the top 100 haha
[dscrdeql] <SwimedDragon> Well, the active ones
[dscrdeql] <c2k> if we belive in the project, no reason not to be in the top 50
[dscrdeql] <c2k> at these prices it doesnt cost a fortune to do so
[dscrdeql] <SwimedDragon> I'm having some Linda I want to sell, but cryptopia has deposits offline because it's upgrading the wallet
[dscrdeql] <SwimedDragon> So you guy's are very lucky if you can buy some now at these prices
[dscrdeql] <c2k> can u sell Linda on barterdex?
[dscrdeql] <SwimedDragon> I don't know
[dscrdeql] <SwimedDragon> I've tried using barterdex with some EQLI but I'm too tech noob for it. Didn't got it working correctly I think
[dscrdeql] <c2k> im not a big fan of bdex either
[dscrdeql] <c2k> doesnt play well with others. lol
[dscrdeql] <KQweci-Mod> We all have to start buying on exrates as they are very cheap now
[dscrdeql] <SwimedDragon> If cryptopia would hurry I might be able to get some extra
[dscrdeql] <KQweci-Mod> In the couple of weeks or months to come we will be going after the dumpers to buy the coins
[dscrdeql] <KQweci-Mod> Sounds good
[dscrdeql] <SwimedDragon> If not, I'll miss it But I already got a nice amount so no worries
[dscrdeql] <c2k> im trying to figure what coins i want to sell to buy more eqli below 10sats
[dscrdeql] <c2k> i will be in there soon to get a bigger bag.
[dscrdeql] <SwimedDragon> Yeah, like I said I have some Linda to sell. I'm only holding a few coins so there's nothing I can really sell instead
[dscrdeql] <c2k> just thiking of how new this project is, with essentially zero visibility, and only a few hunderd people in discord...it makes my mouth water.
imagine when they get some publicity, twitter exposure, discord participants, etc. this will trade over 1000sats quickly
[dscrdeql] <SwimedDragon> Yeah, let's hope so
[dscrdeql] <c2k> the thing is, when it gets there some will want to sell and take profits. but in reality it will just be the beginning of a much bigger move.
[dscrdeql] <c2k> the dico wasnt prices at 20c for no reason. imagine the value of your bag when it gets back THERE!
[dscrdeql] <Jellysmash> hope so
[dscrdeql] <Jellysmash> i'm on board
[dscrdeql] <SwimedDragon> I have to say I do want to sell some to get my initial investment back and take some little profit. But I plan to hold most of my bagg anyway
[dscrdeql] <SwimedDragon> If prices go to like 100 or 1000 sats, I only have to sell like 1% I think to get my investment back and have nice profits
[dscrdeql] <c2k> even if u sold 10% to have a lil extra...thats prob not a bad thing
[dscrdeql] <SwimedDragon> I think even 1% is enough at 1k sats haha
[dscrdeql] <c2k> but if this takes hold in a way the founders envision we will all be very happy
[dscrdeql] <SwimedDragon> But I think it will go insane when btc has it's run again
[dscrdeql] <SwimedDragon> Yeah, I hope I can also buy a dome or house with EQLI when everything is going good
[dscrdeql] <c2k> yes, once btc regains its footing it will take the weight off all crypto
[dscrdeql] <c2k> me too. i want to buy one
[dscrdeql] <c2k> do the founders have a new addition to the team @wark! wark! wark! ???
[dscrdeql] <SwimedDragon> I think that's tipsy dev
[dscrdeql] <c2k> ah right. hahaha
[dscrdeql] <c2k> that tipsy dev...such a sense of humor
[dscrdeql] <c2k>
[dscrdeql] <SwimedDragon> EQLI is the first coin I joined in the early stage
[dscrdeql] <SwimedDragon> So I'm really curious what will happen within the next half year
[dscrdeql] <SwimedDragon> Would be awsome if we look back in 5 years and we're all living in EQLI domes/houses


We build a new model of landscape videos that can be trained on a mixture of static landscape images as well as landscape animations. Our architecture extends StyleGAN model by augmenting it with parts that allow to model dynamic changes in a scene. Once trained, our model can be used to generate realistic time-lapse landscape videos with moving objects and time-of-the-day changes. Furthermore, by fitting the learned models to a static landscape image, the latter can be reenacted in a realistic way. We propose simple but necessary modifications to StyleGAN inversion procedure, which lead to in-domain latent codes and allow to manipulate real images. Quantitative comparisons and user studies suggest that our model produces more compelling animations of given photographs than previously proposed methods. The results of our approach including comparisons with prior art can be seen in the paper, supplementary materials and on the project page.
The architecture of our DeepLandscape model is based on StyleGAN and has four
sets of latent variables $z^{st}$ (encodes colors and the general scene layout), $z^{dyn}$ (encodes global
lighting, e.g. time of day),
$S^{st}$ (a set of square matrices which encode shapes and details of static objects and $S^{dyn}$ (a set of
square matrices which
encode shapes and details of dynamic objects).
The model is trained from two sources of data, the dataset of static scenery images $\mathcal{I}$ and the
dataset of timelapse scenery videos $\mathcal{V}$.
It is relatively easy to collect a large static dataset, while with our best
efforts we were able to collect a few hundreds of videos, that do not cover all
the diversity of landscapes. Thus, both sources of data have to be utilized in
order to build a good model. To do that, we train our generative model in an
adversarial way with two different discriminators.
To create a plausible landscape animation (video), the model should preserve static details (buildings,
fields, mountains) from the original image
and move objects such ad clouds and waves).
We implement the training mode containing two discriminators: $D_{st}$ (static) and $D_{dyn}$ (pairwise
discriminator).
In this work we propose to use a simplified temporal discriminator ($D_{dyn}$), which only looks at
unordered pairs of frames.
We augment the fake set of frames with pairs of crops taken from same
video frame, but from different locations. Since these crops have the same visual
quality as the images in real frames, and since they come from the same videos
as images within real pairs, the pairwise discriminator effectively stops paying
attention to image quality, cannot simply overfit to the statistics of scenes in the
video dataset, and has to focus on finding pairwise inconsistencies within fake
pairs.
To animate a given (real) scenery image $I$, we find (infer) a set of latent variables that produce such
image within the generator.
We perform inference using the following three-step procedure:
Step 1: Predict a set of style vectors $W^{\prime}$ (the notation is described in the paper) using a
feedforward encoder
network.
Step 2: Starting from $\mathcal{W}^\prime$ and zero $\mathcal{S}$, we optimize all latents to improve
reconstruction error.
Step 3: Freezing latents and fine-tuning the weights of $\mathbf{G}$ to further drive
down the reconstruction error.
Lighting manipulation is performed using a separate neural network $\mathbf{A}$ to approximate $\mathbf{M}$
($\mathbf{z}=\mathbf{M}^{-1}(\mathbf{w})$)
To animate scenery images we use spatial maps ($S^{dyn}$): we sample $S^{st}$ and $S^{dyn,1}$ from a unit normal distribution and then warp the $S^{dyn}$ tensor continuously using a homography to obtain $S^{dyn,2}, S^{dyn,3}, ..., S^{dyn,n}$. To change daytime in the video sequence the model uses linear interpolation between two randomly sampled $z^{dyn} \in \mathbf{R}^{D^{dyn}}$ vectors.
The original (real) content image is encoded into $G$'s latent space and animated (with different
daytime styles).
Note: webm format leads to image quality loss
The proposed approach also supports increasing resolution up to 4 times by each side using Super Resolution technique described in the paper.
It is possible to use the trained model to animate out of domain images such as artworks.
E. Logacheva, R. Suvorov, O. Khomenko, A. Mashikhin, and V. Lempitsky. "DeepLandscape: Adversarial Modeling of Landscape Videos" In 2020 Europeran Conference on Computer Vision (ECCV).
or use Bibtex


--- layout: page-breadcrumbs.html title: Facility Locator relatedlinks: - url: http://www.va.gov/directory/guide/PTSD.asp title: Post-Traumatic Stress Disorder (PTSD) Program Locator - url: http://www.va.gov/directory/guide/SUD.asp title: Substance Use Disorder (SUD) Program Locator ---


LEGAL
Only recent, work related information is targeted.
Only the applicant's own statements of fact can contribute to a possible
disqualification - NOT hearsay, profiles, subjective impressions, ratings,
rankings or other arbitrary criteria.
TIME AND COST EFFICIENT
You and your staff conduct the
interviews. There are no additional staff, material or equipment requirements.
The special techniques are easy to learn and use immediately. There are
no special education or background requirements.
ACCEPTABLE
Interviewing is both expected and accepted
by applicants and employers. The special techniques taught are nonintrusive,
non-invasive, and non-accusatory.
VALID AND RELIABLE
Although there is no infallible
pre-employment selection method, the most valid rely on the recent,
work related past to predict future performance and behavior. The
unique procedures encourage information that applicants with
problems withhold - and who knows more about what they've really done
that the applicants themselves.


Name:
Trevor Lawrence Mugo
click the image
to see about me and my projects
==>
PLEASE CLICK ON THE CAR TO READ MORE ABOUT THEM


|
| ||
|
Also see wiki/Wiki, wiki/Help and this forum post.
| ||
|
| ||
| ||
| ||
| ||
|
The new front page puts it all in one place.
| ||
|
DNA FREEDOM | ||
deoxy · news aggregates headlines from 389 diverse sources, and currently contains 5254 links in 18 categories.
Two new links in the page header lead to a google search for sites either related or linked to the current page. A new menu in the footer on some pages provides a new way to jump to similar content. | ||
|
Our private IRC server is online. Services include registered nicknames and channels, on/offline private messaging, total privacy and security, and a growing population of interesting people and useful bots. IRC Server Status gives a current view of activity on the server and channel #deoxy. Details, docs and a java irc client can be found here. Other developments include a dynamic version of the I Ching and the infant deoxywiki. After many months of being unable to update the site and two weeks of DNS mishaps deoxy is finally back online! We're on a new server with lots of capabilities so expect things to get better and more interesting around here.
| ||
| The forums have returned, and Chatter has been re-embedded in a few top level pages. In other news deoxy.org is moving to a powerful new host soon so you can expect amazing new features constantly! | ||
| 2 new zones with 25 documents: Crimethinc and SABBATS. Wrote a chatbox for the forum and embedded it in yippie, deoxy and the root. | ||
|
Added Liberty and the Pursuit of Forbidden Fruit and The Reunification of the Sacred and Natural to PDFA. Cooked up a dynamic mirro r of the hilarious yet sadly defunct Bush or Chimp? Sincere condolences to the victims of terrorism everywhere. The previous update was made on 9-11-01, 3 hours before all hell broke loose, in strict acc ordance with Princeton University's EGG network readings! Like much of the web deoxy is suffering periodic outages due to the latest plague of micro$oft worms. If the site is down just try again later. We've lost another great soul, John Lilly died on September 30, 2001. | ||
| The overhaul continues. Shamanism, Omega, Lilly, RAW, IRC and all Hyperspa ce pages have their links checked with some additions. Philosophos gets fixed too and has audio added as does The Appeal of Anarchy. PDFA keeps gro wing. Enhanced the Alan Watts page with new links and a weekly netcast schedule that allows you to hear him every day of the week! Added Bewilderness, Aphorisms Against Work and a webchat that doesn't require java! | ||
| A complete revision is underway to fix broken links and such. The McKenna and hyperspace chem pages have been cleaned up, the former gaining many new links to excellent resources. A new discussion forum is online and The Partnership for Drug Freedom in America mercilessly blows a few more holes in the already tattered cause of prohibition. | ||
|
Terence McKenna joined the ancestors on April 3, 2000. | ||
| D2K | ||
| A brand new Hyperdiscussion is ready for your use, and reciprocalinks comes of age. Many thanks to the Lycaeum for providing scriptable webspace. | ||
| Complete browsable and searchable Hyperdiscussion archives are now online, over 2800 messages. A new message area based on much more powerful software is in the works! WBAI Powertool v2.0 allows you to persue the world's greatest non-commercial radio station from anywhere on Earth. The live realaudio stream is now very reliable. The McKenna at Wetlands audio file has been restored to functionality. D2K is just around the corner, and you won't believe what's coming early next year. Please go read The Appeal of Anarchy again because it's just that good ! | ||
| Added a transcript of the excellent Art Bell interview Terence did on 4/1/99. Here's a preview of the next update | ||
| Consider The Appeal of Anarchy, and rejoice in The Revolutionary Pleasure of Thinking for Yourself. Made some changes to Omega and Political Corrections. | ||
| Many little fixes and new audio all over the place. Highlights include folding Psychological Heresy into a revamped Thought Crime; improved local Shamanism docs especially The Ayahuas ca-Alien Connection and The Sound of Rushing Water; added A Feeling of Weirdness to the enhanced John Lilly page; additions to the Tryptamine Carriers FAQ and a new DMT nexus; made a simple but powerful frameset to maximize non-linearity; streamlined the main index; cast a futique spell upon The Eightfold Model of Human Consciousness and Music of the Sun; witness the bifurcation of Leary and RAW; added traffic analysis; collected i nformation advisories and warnings; did something about those languishing reciprocalinks; finally modernized The Real McCoy ; generated a new PGP key; and updated WBAI RealAudio information. |


Intergroup differences
For examples and interpretation, see my notebook on intergroup difference explanations.
gshap.intergroup.IntergroupDifference
class gshap.intergroup.IntergroupDifference(group, distance='absolute_mean_distance') [source]
This class measures the distance between distributions of predicted outcomes for different groups.
| Paramters: | group : numpy.array or pandas.Series
(# observations,) array of boolean or binary values indicating group membership. distance : callable or str, default='absolute_mean_distance'
Takes two vectors of model output for the outgroup and ingroup. Output vectors will usually be (# outgroup,) and (# ingroup,), or (# outgroup, # classes) and (# ingroup, # classes). |
|---|---|
| Attributes: | group : numpy.array
Set from the
Set from the |
Examples
import gshap
from gshap.datasets import load_recidivism
from gshap.intergroup import IntergroupDifference
from sklearn.svm import SVC
recidivism = load_recidivism()
X, y = recidivism.data, recidivism.target
clf = SVC().fit(X,y)
g = IntergroupDifference(group=X['black'], distance='relative_mean_distance')
explainer = gshap.KernelExplainer(clf.predict, X, g)
explainer.gshap_values(X, nsamples=10)
Out:
array([ 0.01335252, 0.24884556, 0.00132373, -0.0025238 , -0.00151837,
0.40453822, 0.01636782, 0.07666043, -0.00056414, 0.00966583])
Methods
__call__(self, output) [source]
Compute distance measure between groups.
| Parameters: | ouput : numpy.array or pandas.Series
Model output, usually a (# observations,) or (# observations, # classes) vector. |
|---|---|
| Returns: | distance : scalar
Measure of the distance between the distributions of predicted outputs for outgroup and ingroup observations. |
gshap.intergroup.absolute_mean_distance
def gshap.intergroup.absolute_mean_distance(out_0, out_1) [source]
| Parameters: | out_0 : np.array
(# observations,) vector of model outputs for outgroup observations. out_1 : np.array(# observations,) vector of model outputs for ingroup observations. |
|---|---|
| Returns: | distance : scalar
out_1.mean() - out_0.mean() |
gshap.intergroup.relative_mean_distance
def gshap.intergroup.relative_mean_distance(out_0, out_1) [source]
| Parameters: | out_0 : np.array
(# observations,) vector of model outputs for outgroup observations. out_1 : np.array(# observations,) vector of model outputs for ingroup observations. |
|---|---|
| Returns: | distance : scalar
out_1.mean() / out_0.mean() - 1 |


sediment fluvial Pearl River supply human river change coast climate Phase <. loads coastal period load rating China {border-style impacts px;} #ffffff;} changes natural curves rivers Yang activities delta water Fig. century Yangtze discharge shown zone River, basin increase large observed world’s Supplementary Water significant used Change Figure Hydrol. Zhang [], dam erosion historical land projected px; relative response stations study {font-family Sediment contributions data decrease evolution font-size forcing future predicted studies upper variations Delta, Glob. Planet. Sci. activities, analysis combined construction dams decreases double; effect global impact main measured mega riverflow zones (Fig. (i.e. (–) (–). ./j.gloplacha... ./s--- China, Nat. Res. Yangtan border-color border-width catchment compared computed curve different end high leading likely management reach reduction sea shows solid; station transport use using {font-style {font-weight ./j.jhydrol... Bulletin Climate Coastal Commons Creative Delta Here, Impact Longtan Phases Press, Quat. R.R. Rev. Therefore, anthropogenic associated bold compute courier; deficit driven em; expected followed grid.. heavily information largest material measurement million projections pt;} quantitative remainder resulting river, riverflows year


git-count-objects(1) Manual Page
NAME
git-count-objects - Count unpacked number of objects and their disk consumption
SYNOPSIS
git count-objects [-v] [-H | --human-readable]
DESCRIPTION
This counts the number of unpacked object files and disk space consumed by them, to help you decide when it is a good time to repack.
OPTIONS
- -v
- --verbose
Report in more detail:
count: the number of loose objects
size: disk space consumed by loose objects, in KiB (unless -H is specified)
in-pack: the number of in-pack objects
size-pack: disk space consumed by the packs, in KiB (unless -H is specified)
prune-packable: the number of loose objects that are also present in the packs. These objects could be pruned using git prune-packed.
garbage: the number of files in object database that are not valid loose objects nor valid packs
size-garbage: disk space consumed by garbage files, in KiB (unless -H is specified)
- -H
- --human-readable
Print sizes in human readable format
GIT
Part of the git(1) suite


Hyundai to drive in new sedan Aura with BS VI compliant engines
by PTIHyundai Motor India on Wednesday said its upcoming sedan Aura will be powered by BS VI compliant petrol and diesel engines.
The new powertrains offer low vibrations, superior performance and high fuel efficiency offering cleaner emissions and efficient performance, the company said in a statement. The model would come with a BS VI turbo charged petrol engine, it added. Aura would come with both manual as well as automatic transmissions, Hyundai said.
“Keeping in mind the need of new age customers and future emission norms in India, various amalgamations of petrol and diesel engines with manual and AMT transmission powertrains have been devised for customers to choose from,” Hyundai said. Strong emphasis has been laid on creating advanced after treatment system to meet emission regulations, it added.
Hyundai plans to launch the model in the first quarter of 2020.


2 references to Completed
Microsoft.Management.Infrastructure.CimCmdlets (2)
CimSessionProxy.cs (2)
710
Debug.Assert(this.
Completed
, "Caller should verify that there is no operation in progress");
1824
if (!this.
Completed
)


I mentioned previously that I will be looking at how well different publish and subscribe architectures perform in real time environments. The specific environment that I am looking at is one in which a controller will be controlling, through a broker and a plant, a tiltable plate with a ball on it. this allows for us to analyze the behavior over time and how well it actually performs the task, without any predictive behaviors. The following figures are the basis of my visual analysis, since the ball-plate model is a simulation in the first place. There are really just two things that I am looking at right now, how far the ball is from its desired location in the x and y direction and a histogram of how long the sending of data actually takes.
Over the past few weeks, I have been working on creating network topologies with Mininet[1], then
testing the performance of MQTT QoS 2 in order to run a ball-plate simulation. A ball-plate
simulation is one in which we literally simulate three entities: a ball being balanced on plate, an
observer (plant), and a controller. The observer desires the ball to be in a certain location on the
plate and also in constant motion. The controller just listens the position that the observer wants and
tries to make it happen if it can.
Now the specific network topology only constists of 3 hosts and their respective switches; the hosts are
called Broker, Plant, and Controller. The topology is shown in the diagram below.
Although an extremely basic topology, it will be perfect for simple analysis of the effect of latency and time delay between transmissions for the data between the host, broker, and plant. Originally there were some problems with creating the network topology, because I was not running a controller on my machine.
I have also been looking into some of the features of Mininet, which is an application (or an extension of Linux) that allows you to construct a network and create connections between virtual hosts and servers within a network. Actually, the specific structure that I am wanting to create is one in which there is a publisher, a subscriber, and a broker or a controller, plant, and broker. After I achieve that, I will be adding more and more subscribers and/or publishers and then study the amount of traffic that goes through the network and how real time tasks are affected by the changes on the load within the server and Network. I will then be adding Kafka and MQTT into the mix and comparing the two. Not only comparing generic MQTT and Kafka, but the three levels of MQTT (explained below) and Kafka. Right now, I am currently working more and more with Mininet in order to learn how to accomplish these tasks. [3]
Over the past week I have been looking more into Kafka and MQTT in order to get a better understanding of their purposes and drawbacks. I spoke with my mentor, Dr. Remy, and looked at some of the drawbacks and benefits of these publish and subscribe architecture with respect to the amount of overhead that is associated with each. Overall, it seems that Kafka processes less information in the server and then sends more data in order to achieve the same task on real-time process, whereas MQTT seems to process more within the servers and then send less data overall. Hence the architecture, MQTT, living up to its name of mosquito. MQTT has three levels of service, unlike Kafka which is always guaranteed at least once delivery; MQTT can be at most once but maybe not at all (QoS 0), at least once (QoS 1), and at most once (QoS 2) whereas Kafka is usually only guaranteeing at least once delivery. This creates the possibility for a lot of data being sent over the network, but is also quicker than at most once delivery, because of the lack of a 3 way handshake. [1, 2] I am curious about what the size of the Kafka messages were that were stored on the server, because if the link to the next message is just as long as the message, then we will definitely get a much larger amount of data sent. Overall, it seems that the larger the message being sent in Kafka, the more the overhead shrinks in comparison; in other words, the header does not need to change size when the message size changes by that much so the theoretically optimal thing to do is to have a message of infinite length and then the overhead would be zero. (not really feasible but makes mathematical sense).
Last week I worked on our Networking class project for TCP. I started out with just a basic skeleton file with the basic necessities of establishing a TCP connection between an echo server and an echo client. I made a basic overarching protocol where header information was sent first with the amount of bytes that were going to be sent, and the number of messages that would be sent. With this, I could declare if there were going to be more messages and how many more would be sent. This way there would be no way for the server to close the connection before the client was done communicating with the server. The server then did the simple operation of just inverting the characters in the array; it just switched the case of the letters. This was a toy project just to learn how to use TCP and how to guarantee that the connection does not close before the message is done sending.
This past week I worked on creating software for in depth analysis of the sending and receiving procedure for dweet.io! I started with creating software to time how long it takes for a message to be published and a message to be received when using dweepy. Then, using tcpdump, I analyzed how many messages were sent and received. Using pexpect, a python library for scripting, I ran tcpdump as a child process and used it to analyze things like, how many sends, time between sends and receives, the number of sends and receives, the number of bytes sent, and finally the average ordering of the send receive procedures as well as the percentage of n messages being sent over the trials.
After collecting the data, we can analyze the amount of overhead that exists for sending an integer (4 bytes) and a string of length 10 (10 bytes) for a total of 14 bytes. For both publishing and subscribing, we receive 5,500 bytes and send 1,500 bytes. Also, I looked at the time it takes to get from the dweet.io to the first requests, which doesn't seem to be that long on average. Most of the time was spent during the send and receives between the server and client; there were 16 send/receives in totality.
One problem with my method was that I was waiting 1 second with pexpect in order to see if anymore messages would be sent and received, but this gave the possibility for outliers to be thrown into the mix which resulted in some strange time problems. (Negative time results) Overall, the quality of the results so far is best when looking at the average ordering and the number of bytes sent. In order to improve, I will rerun the trials with a longer delay in between functions in order to catch the outliers that take a long time to send a response.
It is interesting that so much data is actually sent to publish such a small message, but further research must be done to see what can be removed from the process. I will start working with pycurl and implement my own method for communicating with the server and see how that affects the overall times.


Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <doi:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.
| Version: | 0.1.0 |
| Depends: | R (≥ 3.3.1) |
| Imports: | mvtnorm, rootSolve |
| Suggests: | testthat |
| Published: | 2016-09-14 |
| Author: | Lizhen Nie |
| Maintainer: | Lizhen Nie <nie_lizhen at yahoo.com> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | SmartSifter citation info |
| CRAN checks: | SmartSifter results |
| Reference manual: | SmartSifter.pdf |
| Package source: | SmartSifter_0.1.0.tar.gz |
| Windows binaries: | r-devel: SmartSifter_0.1.0.zip, r-release: SmartSifter_0.1.0.zip, r-oldrel: SmartSifter_0.1.0.zip |
| OS X binaries: | r-release: SmartSifter_0.1.0.tgz, r-oldrel: SmartSifter_0.1.0.tgz |
Please use the canonical form https://CRAN.R-project.org/package=SmartSifter to link to this page.


We promise you a new look and more importantly, a new attitude. We build that by getting to know you, your needs and creating the best looking clothes.
And I used a period because contrary to popular belief I strongly dislike exclamation points! We no longer have to be scared of the truth feels good to be home In Roman times the artist would contemplate proportions and colors. Now there is only one important color... Green I even had the pink polo I thought I was Kanye I promise I will never let the people down. I want a better life for all!
Green I even had the pink polo I thought I was Kanye I promise I will never let the people down. I want a better life for all! And I used a period because contrary to popular belief I strongly dislike exclamation points! We no longer have to be scared of the truth feels good to be home In Roman times the artist would contemplate proportions and colors. Now there is only one important color...
I used a period because contrary to popular belief I strongly dislike exclamation points! We no longer have to be scared of the truth feels good to be home In Roman times the artist would contemplate proportions and colors. The 'Gaia' team did a great work while we were collaborating. They provided a vision that was in deep connection with our needs and helped us achieve our goals.
We are keen on creating a second skin for anyone with a sense of style! We design our clothes having our customers in mind and we never disappoint!


Player 1
Here is some data about the player.
Kill
Ace
Assist
Error
Player 2
Here is some data about the player.
Kill
Ace
Assist
Error
Player 3
Here is some data about the player.
Kill
Ace
Assist
Error
Player 4
Here is some data about the player.
Kill
Ace
Assist
Error
Player 5
Here is some data about the player.
Kill
Ace
Assist
Error
Player 6
Here is some data about the player.
Kill
Ace
Assist
Error
Name
Position
Kills
Aces
Digs
Errors
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3
Jonathan Witten
OH
12
4
14
3


p r o s f é r o
Menu
Home
About
Search By Artist
Last Talk Proposed
Artist Name
Donation Amount
Link