Dexter  Goodwin

Dexter Goodwin

1657235700

Level-librarian: Leveldb indexing with A Simple Query Language

level-librarian

This module provides an interface to query a leveldb using a simple query language- LLCJ. Basic operation involves writing documents to the db using an array of index definitions to generate index documents. These index definitions consist of an array of keypaths that reference properties in the document. These keypaths are then used to generate index documents which are used to find the document on read.

LLCJ Query Language

Index definitions

Index definitions consist of an array of keypaths referencing properties of the primary document's value. These keypaths are used to generate index documents. These index documents have a key containing the values found at the index definition's keypath, and a value containing the key of the primary document. Leveldb can then do an alphabetical scan through the index documents and resolve them to primary documents.

Example index definition:

['content.score', 'content.name']

Example primary document:

{
  key: 'w32fwfw33',
  value: {
    timestamp: 1422732866728,
    content: {
      name: 'richard',
      score: 4
    }
  }
}

Index document generated:

{
  key: 4::richard, // (greatly simplified)
  value: 'w32fwfw33'
}

level-librarian scans through the index documents according to a query to find the right document or range of documents. As is usual in levedb, the scan is alphabetical.

Queries:

LLCJ queries consist of an object containing a k property, a v property, and some other optional properties. The k property contains the index definition to be used. Indexes must exist in the db before being queried. The v property specifies an alphabetical range to return.

Examples:

{
  k: ['content.score', 'content.name'],
  v: [5, 'richard']
}

^ This gets all documents with a content.score of 5 and a content.name of 'richard'.

{
  k: ['content.score', 'content.name'],
  v: [5, 'bob']
}

^ This gets all documents with a content.score of 5 and a content.name of 'bob'.

{
  k: ['content.score', 'content.name'],
  v: [5, ['b', 'y']]
}

^ This gets all documents with a content.score of 5 and a content.name between 'b' and 'y'.

{
  k: ['content.score', 'content.name'],
  v: 4
}

^ This gets all documents with a content.score of 4. Results are ordered by content.name.

{
  k: 'timestamp',
  v: [1422732800000, 1422732900000]
}

^ This gets all documents with a timestamp between 1422732800000 and 1422732900000.

Glossary

settings object

The first argument of most of the level-librarian methods is a settings object. This object should contain:

  • db: A leveldb instance
  • index_defs: An array of index definitions
  • level_opts (optional): Options to pass through to leveldb.

document object

A document is an object with a key and a value property. The value will be stored in leveldb under the key.

{
  key: 'w32fwfw33',
  value: {
    timestamp: 1422732866728,
    content: {
      name: 'richard',
      score: 4
    }
  }
}

API

.write(settings, callback)

Arguments:

  • settings: Settings object.
  • callback: Called when done.

This method returns a pull-stream sink that accepts a stream of documents and writes them to the db, also saving index documents based on the index_defs passed in the settings object.

pull(
  pull.values(messages),
  llibrarian.write(settings, function () {
    console.log('done!')
  })
)

.read(settings, query)

Arguments:

  • settings: Settings object.
  • query: Query object.

This method reads from the leveldb based on the query passed. It returns a pull-stream source outputting a stream of documents that match the query. See above for more on querying.

pull(
  llibrarian.read(settings, { k: ['content.score', 'content.name'], v: [5, ['b', 'y']] }),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

.addIndexDocs(index_defs)

Arguments:

  • index_defs: Array of index definitions.

Returns a through stream which injects index documents corresponding to each document in the input stream.

pull(
  pull.values(messages),
  addIndexDocs(settings.index_defs),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

.makeIndexDoc(doc, index_def)

Arguments:

  • doc: Input document.
  • index_def: Single index definition.

Returns an index document generated from doc and index_def.

var doc =   {
  key: 'IB5y8S',
  value: {
    timestamp: 1422732928131,
    content: { score: 5, name: 'zed' }
  }
}

var index_def = ['content.score', 'content.name']

var index_doc = makeIndexDoc(doc, index_def)
// TODO

.makeReadOne()

Arguments:

  • read: Function taking a query and returning a source stream.

This takes a function with the same signature as .read(), and returns a function that reads a single item and calls back.

var readOne = makeReadOne(read)

var query = {
  k: ['content.score', 'content.name'],
  v: 4
}

readOne(query, function (err, doc) {
  console.log(JSON.stringify(doc))
})

.makeWriteOne()

Arguments:

  • write: Function returning a sink stream that writes to the DB.

This takes a function with the same signature as .write() and returns a function that writes one object to the db and calls back.

var writeOne = makeWriteOne(write)

var doc = {
  key: 'zm35bT',
  value: {
    timestamp: 1422732852573,
    content: { score: 5, name: 'ann' }
  }
}

writeOne(doc, function (err) {
  console.log('done!')
})

.resolveIndexDocs(db)

Arguments:

  • db: A leveldb.

This returns a through stream that takes a stream of index documents and resolves them to the primary documents in the db. This is the guts of .read().

pull(
  pl.read(settings.db, range),
  resolveIndexDocs(settings.db),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

Author: Microstar-derp
Source Code: https://github.com/microstar-derp/level-librarian 
License: MIT license

#javascript #leveldb #query 

What is GEEK

Buddha Community

Level-librarian: Leveldb indexing with A Simple Query Language
Cayla  Erdman

Cayla Erdman

1594369800

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

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Dexter  Goodwin

Dexter Goodwin

1657235700

Level-librarian: Leveldb indexing with A Simple Query Language

level-librarian

This module provides an interface to query a leveldb using a simple query language- LLCJ. Basic operation involves writing documents to the db using an array of index definitions to generate index documents. These index definitions consist of an array of keypaths that reference properties in the document. These keypaths are then used to generate index documents which are used to find the document on read.

LLCJ Query Language

Index definitions

Index definitions consist of an array of keypaths referencing properties of the primary document's value. These keypaths are used to generate index documents. These index documents have a key containing the values found at the index definition's keypath, and a value containing the key of the primary document. Leveldb can then do an alphabetical scan through the index documents and resolve them to primary documents.

Example index definition:

['content.score', 'content.name']

Example primary document:

{
  key: 'w32fwfw33',
  value: {
    timestamp: 1422732866728,
    content: {
      name: 'richard',
      score: 4
    }
  }
}

Index document generated:

{
  key: 4::richard, // (greatly simplified)
  value: 'w32fwfw33'
}

level-librarian scans through the index documents according to a query to find the right document or range of documents. As is usual in levedb, the scan is alphabetical.

Queries:

LLCJ queries consist of an object containing a k property, a v property, and some other optional properties. The k property contains the index definition to be used. Indexes must exist in the db before being queried. The v property specifies an alphabetical range to return.

Examples:

{
  k: ['content.score', 'content.name'],
  v: [5, 'richard']
}

^ This gets all documents with a content.score of 5 and a content.name of 'richard'.

{
  k: ['content.score', 'content.name'],
  v: [5, 'bob']
}

^ This gets all documents with a content.score of 5 and a content.name of 'bob'.

{
  k: ['content.score', 'content.name'],
  v: [5, ['b', 'y']]
}

^ This gets all documents with a content.score of 5 and a content.name between 'b' and 'y'.

{
  k: ['content.score', 'content.name'],
  v: 4
}

^ This gets all documents with a content.score of 4. Results are ordered by content.name.

{
  k: 'timestamp',
  v: [1422732800000, 1422732900000]
}

^ This gets all documents with a timestamp between 1422732800000 and 1422732900000.

Glossary

settings object

The first argument of most of the level-librarian methods is a settings object. This object should contain:

  • db: A leveldb instance
  • index_defs: An array of index definitions
  • level_opts (optional): Options to pass through to leveldb.

document object

A document is an object with a key and a value property. The value will be stored in leveldb under the key.

{
  key: 'w32fwfw33',
  value: {
    timestamp: 1422732866728,
    content: {
      name: 'richard',
      score: 4
    }
  }
}

API

.write(settings, callback)

Arguments:

  • settings: Settings object.
  • callback: Called when done.

This method returns a pull-stream sink that accepts a stream of documents and writes them to the db, also saving index documents based on the index_defs passed in the settings object.

pull(
  pull.values(messages),
  llibrarian.write(settings, function () {
    console.log('done!')
  })
)

.read(settings, query)

Arguments:

  • settings: Settings object.
  • query: Query object.

This method reads from the leveldb based on the query passed. It returns a pull-stream source outputting a stream of documents that match the query. See above for more on querying.

pull(
  llibrarian.read(settings, { k: ['content.score', 'content.name'], v: [5, ['b', 'y']] }),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

.addIndexDocs(index_defs)

Arguments:

  • index_defs: Array of index definitions.

Returns a through stream which injects index documents corresponding to each document in the input stream.

pull(
  pull.values(messages),
  addIndexDocs(settings.index_defs),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

.makeIndexDoc(doc, index_def)

Arguments:

  • doc: Input document.
  • index_def: Single index definition.

Returns an index document generated from doc and index_def.

var doc =   {
  key: 'IB5y8S',
  value: {
    timestamp: 1422732928131,
    content: { score: 5, name: 'zed' }
  }
}

var index_def = ['content.score', 'content.name']

var index_doc = makeIndexDoc(doc, index_def)
// TODO

.makeReadOne()

Arguments:

  • read: Function taking a query and returning a source stream.

This takes a function with the same signature as .read(), and returns a function that reads a single item and calls back.

var readOne = makeReadOne(read)

var query = {
  k: ['content.score', 'content.name'],
  v: 4
}

readOne(query, function (err, doc) {
  console.log(JSON.stringify(doc))
})

.makeWriteOne()

Arguments:

  • write: Function returning a sink stream that writes to the DB.

This takes a function with the same signature as .write() and returns a function that writes one object to the db and calls back.

var writeOne = makeWriteOne(write)

var doc = {
  key: 'zm35bT',
  value: {
    timestamp: 1422732852573,
    content: { score: 5, name: 'ann' }
  }
}

writeOne(doc, function (err) {
  console.log('done!')
})

.resolveIndexDocs(db)

Arguments:

  • db: A leveldb.

This returns a through stream that takes a stream of index documents and resolves them to the primary documents in the db. This is the guts of .read().

pull(
  pl.read(settings.db, range),
  resolveIndexDocs(settings.db),
  pull.collect(function (err, arr) {
    console.log(arr)
  })
)

Author: Microstar-derp
Source Code: https://github.com/microstar-derp/level-librarian 
License: MIT license

#javascript #leveldb #query 

Ahebwe  Oscar

Ahebwe Oscar

1620185280

How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

**Read More : **How to make Chatbot in Python.

Read More : Django Admin Full Customization step by step

let’s just get into this diagram that I made so in here:

django queries aboutDescribe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

Dexter  Goodwin

Dexter Goodwin

1657228320

Level-indexer: Generic Property indexer for Leveldb

level-indexer

Generic indexer for leveldb. Only stores document keys for space efficiency.

npm install level-indexer

Usage

var indexer = require('level-indexer')

// create a index (by country)
var country = indexer(db, ['country']) // index by country

country.add({
  key: 'mafintosh',
  name: 'mathias',
  country: 'denmark'
})

country.add({
  key: 'maxogden',
  name: 'max',
  country: 'united states'
})

var stream = country.find({
  gte:{country:'denmark'},
  lte:{country:'denmark'}
})

// or using the shorthand syntax

var stream = country.find('denmark')

stream.on('data', function(key) {
  console.log(key) // will print mafintosh
})

The stored index is prefix with the index key names which means you can use the same levelup instance to store multiple indexes.

API

index = indexer(db, [prop1, prop2, ...], [options])

Creates a new index using the given properties. Options include

{
  map: function(key, cb) {
    // map find results to another value
    db.get(key, cb)
  }
}

index.add(doc, [key], [cb])

Add a document to the index. The document needs to have a key or provide one. Only the key will be stored in the index.

index.remove(doc, [key], [cb])

Remove a document from the index.

index.key(doc, [key])

Returns the used leveldb key. Useful if you want to batch multiple index updates together yourself

var batch = [{type:'put', key:index.key(doc), value:doc.key}, ...]

stream = index.find(options, [cb])

Search the index. Use options.{gt,gte,lt,lte} to scope your search.

// find everyone in the age range 20-50 in denmark

var index = indexer(db, ['country', 'age'])

...
var stream = index.find({
  gt: {
    country: 'denmark',
    age: 20
  },
  lt: {
    country: 'denmark',
    age: 50
  }
})

Optionally you can specify the ranges using arrays

var stream = index.find({
  gt: ['denmark', 20],
  lt: ['denmark', 50]
})

Or if you do not care about ranges

var stream = index.find(['denmark', 20])

// equivalent to

var stream = index.find({
  gte: ['denmark', 20],
  lte: ['denmark', 20]
})

The stream will contain the keys of the documents that where found in the index. Use options.map to map the to the document values.

Options also include the regular levelup db.createReadStream options.

If you set cb the stream will be buffered and passed as an array.

index.findOne(options, cb)

Only find the first match in the index and pass that to the callbck

Author: Mafintosh
Source Code: https://github.com/mafintosh/level-indexer 
License: MIT license

#javascript #index #leveldb 

Ananya Gupta

Ananya Gupta

1594464365

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