_type: few_shot
example_prompt:
  _type: prompt
  input_types: {}
  input_variables:
  - answer
  - context
  - query
  - rules
  metadata: null
  name: null
  output_parser: null
  partial_variables: {}
  tags: null
  template: "\n<Question> {query}\n \n<Context> {context}\n \n<Rules> {rules}\n \n\
    <Answer> {answer}\n"
  template_format: f-string
  validate_template: false
example_selector: null
example_separator: '


  '
examples:
- answer: "# Dense Cells\nStep 1: Define a set of grid points and assign the given\
    \ data points on the grid.\nStep 2: Determine the dense and sparse cells. If the\
    \ number of points in a cell exceeds the threshold value t, the cell is categorized\
    \ as dense cell. Sparse cells are removed from the list.\nStep 3: Merge the dense\
    \ cells if they are adjacent.\nStep 4: Form a list of grid cells for every subspace\
    \ as output.\n\n#CILQUE\n **Stage 1:**\n- Step 1: Identify the dense cells.\n\
    - Step 2: Merge dense cells c\u2081 and c\u2082 if they share the same interval.\n\
    - Step 3: Generate a particle rule to generate (k + 1)th cell for higher dimension.\
    \ Then, check whether the number of points cross the threshold. This is repeated\
    \ till there are no dense cells or new generation of dense cells.\n\n**Stage 2:**\n\
    - Step 1: Merging of dense cells into a cluster is carried out in each subspace\
    \ using maximal regions to cover dense cells. The maximal region is an hyperrectangle\
    \ where all cells fall into.\n- Step 2: Maximal region tries to cover all dense\
    \ cells to form clusters.\n\n # DBSCAN\n- Step 1: Randomly select a point p. Compute\
    \ distance between p and all other points.\n- Step 2: Find all points from p with\
    \ respect to its neighborhood and check whether it has minimum number of points\
    \ m. If so, it is marked as a core point.\n- Step 3: If it is a core point, then\
    \ a new cluster is formed, or existing cluster is enlarged.\n- Step 4: If it is\
    \ a border point, then the algorithm moves to the next point and marks it as visited.\n\
    - Step 5: If it is a noise point, they are removed.\n- Step 6: Merge the clusters\
    \ if it is mergeable, dist (c, c) < \u025B.\n- Step 7: Repeat the process 3-6\
    \ till all points are processed."
  context: "DBSACN\nStep 1: Randomly select a point p. Compute distance between P\
    \ and ail other points '\nStep 2: Find all points]from p with respect to its neighbourhoud\
    \ and check whether it has minimum number of points m. If 80, it is marked as\
    \ a core point\nStep 3: If it is a core point, then a new cluster is formed, or\
    \ existing cluster 1s enlarged.\nStep 4: [fit is a border point, then the algorithm\
    \ moves to the next point and marks it as visited\nStep 5: If it is a noise point,\
    \ they are removed.\nStep 6: Merge the clusters if it is mergeable, dist (cc )<\
    \ \xA2.\nStep 7: Repeat the process 3-6 till all Points are processed. \n\nDense\
    \ Cell\nStep 1: Defining a set of grid points and assigning the given data points\
    \ on the grid.\nStep 2: Determine the dense and sparse cells. lf the number of\
    \ points in a cell exceeds the threshold\nvalue t, the cell is categorized as\
    \ a dense cell. Sparse cells are removed from the list.\nStep 3: Merge the dense\
    \ cells if they are adjacent.\nStep 4: Form a list of grid cells for every subspace\
    \ as output.\n\nCLIQUE\nStage 1\nStep 1: Identify the dense cells\nStep 2: Merge\
    \ dense cells c. and c, if they share the same interval.\nStep 3: Generate Apriori\
    \ rule to generate (k + 1)\" cell tor higher dimension. Then, check\nwhether the\
    \ number of points across the threshold This 1s repeated till there are no\ndense\
    \ cells or a new generation of dense cells\n\nStage 2\nStep 1: Merging of dense\
    \ cells into a cluster is carried out in each subspace using maximal regions to\
    \ cover dense cells The maximal region is a hyperrectangle where all cells fall\
    \ into.\nStep 2; Maximal region tries to cover all dense cells to form clusters."
  query: Assess DBSCAN, Dense cells and CLIQUE with appropriate steps. (8 marks)
  rules: "- If the question says answer for X number of marks, you have to provide\
    \ X number of points.\n - Each point has to be explained in 3-4 sentences.\n -\
    \ In case the context express a mathematical equation, provide the equation in\
    \ LaTeX format as shown in the example.\n - In case the user requests for a code\
    \ snippet, provide the code snippet in the language specified in the example.-\
    \ If the user requests to summarise or use the previous message as context ignoring\
    \ the explicit context given in the message."
- answer: 'Sharding is a technique for dividing a large database into smaller, manageable
    parts called shards, which are stored across multiple servers or nodes. This process
    enhances scalability, performance, and fault tolerance by distributing data and
    processing load. Sharding works by partitioning data based on criteria like geographic
    location, user ID, or time period, and each shard is responsible for a subset
    of the data. This method allows for horizontal scaling, improving the system''s
    capacity to handle large volumes of data and requests efficiently.


    The system uses a shard key to identify which shard contains the required data
    for a query. The shard key is a unique identifier that maps data to its corresponding
    shard. Upon receiving a query, the system determines the appropriate shard and
    forwards the query to the correct server or node.


    **Features of Sharding:**

    - Sharding makes the database smaller, faster, and more manageable.

    - It can be complex to implement.

    - Sharding reduces transaction costs and allows each shard to read and write its
    own data.

    - Many NoSQL databases offer auto-sharding.

    - Failure of one shard does not affect the data processing of other shards.


    **Benefits of Sharding:**

    1. **Improved Scalability:** Sharding allows horizontal scaling by adding more
    servers or nodes, enhancing the system''s capacity to handle large volumes of
    data and requests.

    2. **Increased Performance:**By distributing data across multiple servers or nodes,
    sharding improves performance, resulting in faster response times and better throughput.

    3. **Fault Tolerance:** Sharding provides fault tolerance as the system can continue
    to function even if one or more servers or nodes fail, thanks to data replication
    across multiple servers or nodes.

    4. **Reduced Costs:** Horizontal scaling with sharding can be more cost-effective
    than vertical scaling by upgrading hardware, as it can be done using commodity
    hardware, which is typically less expensive than high-end servers.'
  context: "It is a very important concept that helps the system to keep data in different\
    \ resources\naccording to the sharding process. The word \u201CShard\u201D means\
    \ \u201Ca small part of a\nwhole\u201C. Hence Sharding means dividing a larger\
    \ part into smaller parts. In DBMS,\nSharding is a type of DataBase partitioning\
    \ in which a large database is divided or\n\npartitioned into smaller data and\
    \ different nodes. These shards are not only smaller,\nbut also faster and hence\
    \ easily manageable.\nHow does Sharding work?\nIn a sharded system, the data is\
    \ partitioned into shards based on a predetermined\ncriterion. For example, a\
    \ sharding scheme may divide the data based on geographic\nlocation, user ID,\
    \ or time period. Once the data is partitioned, it is distributed across\nmultiple\
    \ servers or nodes. Each server or node is responsible for storing and processing\
    \ a\nsubset of the data.\nExample:\n\nTo query data from a sharded database, the\
    \ system needs to know which shard contains\nthe required data. This is achieved\
    \ using a shard key, which is a unique identifier that is\nused to map the data\
    \ to its corresponding shard. When a query is received, the system\nuses the shard\
    \ key to determine which shard contains the required data and then sends\nthe\
    \ query to the appropriate server or node.\nFeatures of Sharding:\n\uF0B7 Sharding\
    \ makes the Database smaller\n\uF0B7 Sharding makes the Database faster\n\uF0B7\
    \ Sharding makes the Database much more easily manageable\n\uF0B7 Sharding can\
    \ be a complex operation sometimes\n\uF0B7 Sharding reduces the transaction cost\
    \ of the Database\n\uF0B7 Each shard reads and writes its own data.\n\uF0B7 Many\
    \ NoSQL databases offer auto-sharding.\n\uF0B7 Failure of one shard doesn\u2019\
    t effect the data processing of other shards.\nBenefits of Sharding:\n1. Improved\
    \ Scalability: Sharding allows the system to scale horizontally by adding more\n\
    servers or nodes as the data grows. This improves the system\u2019s capacity to\
    \ handle\nlarge volumes of data and requests.\n\n2. Increased Performance: Sharding\
    \ distributes the data across multiple servers or\nnodes, which improves the system\u2019\
    s performance by reducing the load on each server\nor node. This results in faster\
    \ response times and better throughput.\n3. Fault Tolerance: Sharding provides\
    \ a degree of fault tolerance as the system can\ncontinue to function even if\
    \ one or more servers or nodes fail. This is because the data\nis replicated across\
    \ multiple servers or nodes, and if one fails, the others can continue\nto serve\
    \ the requests.\n4. Reduced Costs: Sharding allows the system to scale horizontally,\
    \ which can be more\ncost-effective than scaling vertically by upgrading hardware.\
    \ This is because horizontal\nscaling can be done"
  query: Explain sharding in system design along with its benefits. (10 marks)
  rules: "- If the question says answer for X number of marks, you have to provide\
    \ X number of points.\n - Each point has to be explained in 3-4 sentences.\n -\
    \ In case the context express a mathematical equation, provide the equation in\
    \ LaTeX format as shown in the example.\n - In case the user requests for a code\
    \ snippet, provide the code snippet in the language specified in the example.-\
    \ If the user requests to summarise or use the previous message as context ignoring\
    \ the explicit context given in the message.\n"
input_types: {}
input_variables:
- context
- query
- rules
metadata: null
name: null
output_parser: null
partial_variables: {}
prefix: "\nYou are assisting a student to understand topics.\n \nYou have to answer\
  \ the below question by utilising the below context to answer the question.\nNote\
  \ to follow the rules given below.\n"
suffix: "\n<Question> {query}\n \n<Context> {context}\n \n<Rules> {rules}\n <Answer>"
tags: null
template_format: f-string
validate_template: false