Julia Programming

Julia Programming

1661790027

LevelDB.jl: Julia interface to Google's LevelDB Key Value Database

LevelDB 

LevelDB is Google's open source on-disk key-value storage library that provides an ordered mapping from string keys to binary values. In many applications where only key based accesses are needed, it tends to be a faster alternative than databases. LevelDB was written in C++ with a C calling API included. This module provides a Julia interface to LevelDB using Julia's ccall mechanism.

Install LevelDB

You can build LevelDB from its source code at https://github.com/google/leveldb. Please install the final dynamic library into a system directory such as /usr/lib or make sure libleveldb.so is in one of your LD_LIBRARY_PATH directories. If libleveldb.so is not installed, Julia will try to download and build it automatically.

Run Testing Code

(v1.1) pkg> test LevelDB

This will exercise batched and non-batched writes and reads for string and float array values.

Create/Open/Close a LevelDB database

julia> db = LevelDB.DB(file_path; create_if_missing = false, error_if_exists = false)

Here file_path is the full path to a directory that hosts a LevelDB database. create_if_missing is a boolean flag when true the database will be created if it does not exist. error_if_exists is a boolean flag when true an error will be thrown if the database already exists. The return value is a database object for passing to read/write calls.

julia> close(db)

Close a database, db is the object returned from a LevelDB.DB call. A directory can only be opened by a single LevelDB.DB at a time.

Read and Write Operations

julia> db[key] = value

key and value are Array{UInt8}.

julia> db[key]

Return value is an Array{UInt8}, one can use the reinterpret function to cast it into the right array type (see test code).

julia> delete!(db, key)

Delete a key from db.

Batched Write

LevelDB supports grouping a number of put operations into a write batch, the batch will either succeed as a whole or fail altogether, behaving like an atomic update.

julia> db[keys] = values

keys and values must behave like iterators returning Array{UInt8}. Creates a write batch internally which is then commited to db.

Iterate

julia> for (key, value) in db
           #do something with the key value pair
       end

Iterate over all key => value pairs in a LevelDB.DB.

julia> for (key, value) in LevelDB.RangeView(db, key1, key2)
           #do something with the key value pair
       end

Iterate over a range between key1 and key2 (inclusive)

Authors

  • Jerry Zhenlei Cai ( jpenguin at gmail dot com )
  • Guido Kraemer

additional contributions by

  • @huwenshuo
  • @tmlbl

Download Details:

Author: jerryzhenleicai
Source Code: https://github.com/jerryzhenleicai/LevelDB.jl 
License: View license

#julia #leveldb #interface #database 

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LevelDB.jl: Julia interface to Google's LevelDB Key Value Database
Julia Programming

Julia Programming

1661790027

LevelDB.jl: Julia interface to Google's LevelDB Key Value Database

LevelDB 

LevelDB is Google's open source on-disk key-value storage library that provides an ordered mapping from string keys to binary values. In many applications where only key based accesses are needed, it tends to be a faster alternative than databases. LevelDB was written in C++ with a C calling API included. This module provides a Julia interface to LevelDB using Julia's ccall mechanism.

Install LevelDB

You can build LevelDB from its source code at https://github.com/google/leveldb. Please install the final dynamic library into a system directory such as /usr/lib or make sure libleveldb.so is in one of your LD_LIBRARY_PATH directories. If libleveldb.so is not installed, Julia will try to download and build it automatically.

Run Testing Code

(v1.1) pkg> test LevelDB

This will exercise batched and non-batched writes and reads for string and float array values.

Create/Open/Close a LevelDB database

julia> db = LevelDB.DB(file_path; create_if_missing = false, error_if_exists = false)

Here file_path is the full path to a directory that hosts a LevelDB database. create_if_missing is a boolean flag when true the database will be created if it does not exist. error_if_exists is a boolean flag when true an error will be thrown if the database already exists. The return value is a database object for passing to read/write calls.

julia> close(db)

Close a database, db is the object returned from a LevelDB.DB call. A directory can only be opened by a single LevelDB.DB at a time.

Read and Write Operations

julia> db[key] = value

key and value are Array{UInt8}.

julia> db[key]

Return value is an Array{UInt8}, one can use the reinterpret function to cast it into the right array type (see test code).

julia> delete!(db, key)

Delete a key from db.

Batched Write

LevelDB supports grouping a number of put operations into a write batch, the batch will either succeed as a whole or fail altogether, behaving like an atomic update.

julia> db[keys] = values

keys and values must behave like iterators returning Array{UInt8}. Creates a write batch internally which is then commited to db.

Iterate

julia> for (key, value) in db
           #do something with the key value pair
       end

Iterate over all key => value pairs in a LevelDB.DB.

julia> for (key, value) in LevelDB.RangeView(db, key1, key2)
           #do something with the key value pair
       end

Iterate over a range between key1 and key2 (inclusive)

Authors

  • Jerry Zhenlei Cai ( jpenguin at gmail dot com )
  • Guido Kraemer

additional contributions by

  • @huwenshuo
  • @tmlbl

Download Details:

Author: jerryzhenleicai
Source Code: https://github.com/jerryzhenleicai/LevelDB.jl 
License: View license

#julia #leveldb #interface #database 

Machine Learning in Rust

Machine Learning in Rust

1619247660

Google's TPU's being primed for the Quantum Jump

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

The liquid-cooled Tensor Processing Units, built to slot into server racks, can deliver up to 100 petaflops of compute.

As the world is gearing towards more automation and AI, the need for quantum computing has also grown exponentially. Quantum computing lies at the intersection of quantum physics and high-end computer technology, and in more than one way, hold the key to our AI-driven future.

Quantum computing requires state-of-the-art tools to perform high-end computing. This is where TPUs come in handy. TPUs or Tensor Processing Units are custom-built ASICs (Application Specific Integrated Circuits) to execute machine learning tasks efficiently. TPUs are specific hardware developed by Google for neural network machine learning, specially customised to Google’s Machine Learning software, Tensorflow.

The liquid-cooled Tensor Processing units, built to slot into server racks, can deliver up to 100 petaflops of compute. It powers Google products like Google Search, Gmail, Google Photos and Google Cloud AI APIs.

#opinions #alphabet #asics #floq #google #google alphabet #google quantum computing #google tensorflow #google tensorflow quantum #google tpu #google tpus #machine learning #quantum computer #quantum computing #quantum computing programming #quantum leap #sandbox #secret development #tensorflow #tpu #tpus

Savannah Deckow

Savannah Deckow

1624575120

Java: How to Get Keys and Values from a Map

Introduction

Key-value stores are essential and often used, especially in operations that require fast and frequent lookups. They allow an object - the key - to be mapped to another object, the value. This way, the values can easily be retrieved, by looking up the key.

In Java, the most popular Map implementation is the HashMap class. Aside from key-value mapping, it’s used in code that requires frequest insertions, updates and lookups. The insert and lookup time is a constant O(1).

In this tutorial, we’ll go over how to get the Keys and Values of a map in Java.

#java #java: how to get keys and values from a map #keys #map #values #how to get keys and values from a map

Julia Programming

Julia Programming

1661786100

DBInterface.jl: Database interface definitions for Julia

DBInterface.jl  

Purpose

DBInterface.jl provides interface definitions to allow common database operations to be implemented consistently across various database packages.

For Users

To use DBInterface.jl, select an implementing database package, then utilize the consistent DBInterface.jl interface methods:

conn = DBInterface.connect(T, args...; kw...) # create a connection to a specific database T; required parameters are database-specific

stmt = DBInterface.prepare(conn, sql) # prepare a sql statement against the connection; returns a statement object

results = DBInterface.execute(stmt) # execute a prepared statement; returns an iterator of rows (property-accessible & indexable)

rowid = DBInterface.lastrowid(results) # get the last row id of an INSERT statement, as supported by the database

# example of using a query resultset
for row in results
    @show propertynames(row) # see possible column names of row results
    row.col1 # access the value of a column named `col1`
    row[1] # access the first column in the row results
end

# results also implicitly satisfy the Tables.jl `Tables.rows` inteface, so any compatible sink can ingest results
df = DataFrame(results)
CSV.write("results.csv", results)

results = DBInterface.execute(conn, sql) # convenience method if statement preparation/re-use isn't needed

stmt = DBInterface.prepare(conn, "INSERT INTO test_table VALUES(?, ?)") # prepare a statement with positional parameters

DBInterface.execute(stmt, [1, 3.14]) # execute the prepared INSERT statement, passing 1 and 3.14 as positional parameters

stmt = DBInterface.prepare(conn, "INSERT INTO test_table VALUES(:col1, :col2)") # prepare a statement with named parameters

DBInterface.execute(stmt, (col1=1, col2=3.14)) # execute the prepared INSERT statement, with 1 and 3.14 as named parameters

DBInterface.executemany(stmt, (col1=[1,2,3,4,5], col2=[3.14, 1.23, 2.34 3.45, 4.56])) # execute the prepared statement multiple times for each set of named parameters; each named parameter must be an indexable collection

results = DBInterface.executemultiple(conn, sql) # where sql is a query that returns multiple resultsets

# first iterate through resultsets
for result in results
    # for each resultset, we can iterate through resultset rows
    for row in result
        @show propertynames(row)
        row.col1
        row[1]
    end
end

DBInterface.close!(stmt) # close the prepared statement
DBInterface.close!(conn) # close connection

For Database Package Developers

See the documentation for expanded details on required interface methods.

Download Details:

Author: JuliaDatabases 
Source Code: https://github.com/JuliaDatabases/DBInterface.jl 
License: View license

#julia #database #interface 

Ruth Nabimanya

Ruth Nabimanya

1620633584

System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database