add 3-2, took 2hr and more
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#### Running
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- Shuffle and sort
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- Every intermediate key-value pairs generated by mapper are collected
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- Same key items are grouped to a list of values
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- Shuffle, moving data from mappers to reducers and sort, ordering outputs
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before being processed by reducer
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- Shuffle:
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- Every intermediate key-value pairs generated by mapper are
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**collected**
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- Pairs are **partitioned** to a list of values, each partition sorted
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by key
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- Combiner run on each partition, to combine the key-value pairs with
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key- list of values
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- Data is shuffled and sent to each reducer
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- Data provided to each reducer is sorted by keys
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- Sort:
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- Reducer copy data from mapper
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- Downloaded data are **merged** and **sorted** as input for reducer, key-list
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of values, sorted by keys
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- Runtime
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- Partitions input
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- schedules executions
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- Schedules executions
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- Handles load balancing
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- Shuffle, partition, sort intermediate data
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- Handle failure
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211
3-2-hadoop.md
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211
3-2-hadoop.md
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# Hadoop Architecture
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## Features
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- Designed to run in clusters of pcs
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- Scales up linearly
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- Suitable for local networks or data centers
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- Design principles
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- Data is distributed around the network
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- Computation is sent to data: code is sent to run on nodes
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- Basic architecture is mater / worker
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- Offers the following:
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- Redundant, fault tolerant data storage
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- Parallel computation framework
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- Job coordination
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## Structure of a MapReduce job
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- **Job**: a program to be executed across the **entire** dataset
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- Packaged as a jar file, with all the code needed
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- Job is assigned a cluster-unique ID
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- Data attached to job is replicated over the entire internet
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- **Task**: an execution on a slice of data
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- **Task Attempt**: An instance of a local execution
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## Job execution flow
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- Split data into computing chunks
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- Assign a chunk to node manager
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- Run many mappers
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- [Shuffle and sort](/3-1-map-reduce.md#running)
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- Run many reducers
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- Result from reducers create the job output
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## The Optional Combiner
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- The bottleneck in map-reduce frameworks:
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- Map and reduce jobs scale close to linearly, so they are not
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- Potential bottleneck at the shuffle and sort operations (between map and
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reduce)
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- Data need to be copied over network
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- Lots of keys are emitted by mapper, and sorting they are costly
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- Combiner can be used to execute before shffling and sorting
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- Reason
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- Acts as a preliminary reducer
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- Executed at each mapper node, just before sending all the pairs for
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shuffling
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- **Reduces** amount of data emitted by mapper, to improve efficiency
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- Restrictions and rules:
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- **Cannot** be mandatory, the job should work the correctly without it
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- **Idempotent**: The number of time the combiner is applied shouldn't
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change the output
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- No **Side effect**, or they won't be idempotent
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- **Preserve** the keys: can't change the keys to disrupt the **sort**
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order, or changing the **partitioning**
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- Example of using reducer code as the combiner:
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```java
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public void Combine(String key, List<Integer> values) {
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int sum = 0;
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for (Integer count: values){
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sum+=count;
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}
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emit(key, sum);
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}
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```
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- TODO review the combiner diagram
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## Apache Hadoop
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### Architecture of Hadoop
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- Executes on nodes connected by network
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- Each node runs a set of daemons
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- Computing:
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- `ResourceManager`
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- `NodeManager`
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- Storage:
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- `NameNode`
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- `SecondaryNameNode` as backup
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- `DataNode`
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- Nodes are in Master Slave architecture
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- Master node: `NameNode`, `ResourceManager`
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- Aware of slave nodes
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- Receives external requests
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- Decide the work split of slaves
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- Notify slaves
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- Slave node, also called Worker node: `DataNode`, `NodeManager`
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- Executes the tasks, received from master
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### What Hadoop Does
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- Resource Management: the existence and availability of resources
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- Job Allocation: needed resources for job, and the split of work
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- Job Execution: Run job, make sure it's completed, deal with failures
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## Job execution: YARN
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### Intro
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- Estimate how many map and reduce tasks are needed for a job, based on _input
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dataset_ and _job definition_
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- Ideally, one different node for each map / reduce tasks
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### Deciding the number of workers
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#### Mapper Parallelization
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- Different input split are processed on each mapper
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- Input data size is known
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- Number of mappers: Input size $/$ Split Size
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- If input size is small, and has many files, they won't be splitted and
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will use more mappers
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#### Reducer
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- Number of reducer is **user defined**, since it's hard to figure out
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automatically
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- Keys are partitioned, partitioning too much lead to overhead in shuffle
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and sort
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### Execution Daemons
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#### `ResourceManager`
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- On master: one per cluster
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- Responsibility:
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- Receive job requests from client
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- Create a `ApplicationMaster` per job to manage it
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- Allocate **container** in slave nodes, with the assigned resources
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- Provision the health of `NodeManager` nodes
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#### `NodeManager`
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- Responsibility:
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- Coordinate the execution of tasks on node
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- Send health information to `ResourceManager`
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#### `ApplicationMaster`
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- Only one per job
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- Responsibility: Job allocation and job execution
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- Implements specific framework, for example MapReduce
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- Negitiates with `ResourceManager` on the resources required
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- Decides which node will run which job, in the container, given by
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`ResourceManager`
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- Destroyed when job completed
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## Storage: HDFS
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### Definition
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- Hadoop Distributed File System
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- This is the storage for Hadoop's input and output
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- Features:
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- Tailored for MapReduce jobs
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- Large Block size (64MB)
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- Not a POSIX compliant file system
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### Data distribution: key element of map reduce
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- Job code (jars) moved to where data is stored
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- Blocks are replicated on the cluster, by default **three** times, to ensure
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**reliability**
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### Storage daemon
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- `DataNode`: many per cluster
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- Stores block from HDFS
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- Report the blocks stored to `NameNode`
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- `NameNode`: one per cluster
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- Keep index and location for every block
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- Don't do computation, because this is heavy
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- Single point of failure
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- `SecondaryNameNode`
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- Communicates directly with a `NameNode`
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- Store backup of index table
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### Data Replication
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- Format: csv, example:
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| `Filename` | `numReplicas` | `block-ids` |
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| ---------- | ------------- | ----------- |
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| part-0 | r:2 | {1,3} |
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| part-1 | r:3 | {2,4,5} |
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- Definition: Creating and maintaining multiple copies of data, across different
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nodes in the HDFS
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- Significance
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- Fault tolerance
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- Data availability
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- System Reliability
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- Support Parallel Processing
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### Failure recovery
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- Identifying failure: by missing the hearbeat signal
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- Replication: NameNode initializes replication once a node failure is detected
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- Maintaining Integrity: use a predetermined replication factor
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- Mitigating Potential Disruptions: Dynamic data management
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### Operation
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- TODO: See the graph in p59
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- Input data:
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- Mappers are assigned input splits from HDFS input path (default 64MB)
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- Data locality: `ApplicationMaster` try to assign mapper where data is
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stored
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- Output data:
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- Copied to HDFS, one file per reducer
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- Replicate
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