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  DSpace
  
  
  Now downloading Szlendakova_ISU_2003_S95.pdf...
  
  
  
  The process of retrieving scholarly documents from institutional repositories has
  become increasingly streamlined with the adoption of open‑access platforms such as DSpace.
  When a user initiates a download—such as the PDF titled Szlendakova_ISU_2003_S95.pdf—the system triggers several backend operations that ensure
  both security and accessibility.
  
  
  
  
  1. Authentication and Authorization
  
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  has the necessary permissions. This step may involve checking institutional credentials,
  membership status, or other access controls configured by repository
  administrators. If the user lacks appropriate rights,
  the system will either redirect them to a
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  2. Logging and Auditing
  
  Each download request is recorded in server logs for compliance purposes.
  
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  User identifier
  
  
  Timestamp of the request
  
  
  Resource requested (e.g., `article_12345.pdf`)
  
  
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  file from persistent storage. Depending on configuration:
  
  
  
  
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  4. Post‑Delivery Logging
  
  After transmission completes, the server updates any counters (e.g.,
  view counts) and records the download event in an analytics database for later reporting.
  
  
  
  
  ---
  
  
  
  
  3. Data Model Design
  
  
  Below is a simplified relational schema representing
  the core entities: User, Dataset, File, and
  Organization. The design emphasizes extensibility, auditability, and compliance
  with privacy regulations.
  
  
  
  
  -- Organization (e.g., university, research institute)
  CREATE TABLE organization (
  org_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  name VARCHAR(255) NOT NULL UNIQUE,
  address TEXT,
  contact_email VARCHAR(254),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT
  CURRENT_TIMESTAMP
  );
  
  -- User (contributors, curators)
  CREATE TABLE app_user (
  user_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  org_id BIGINT REFERENCES organization(org_id) ON DELETE
  SET NULL,
  username VARCHAR(50) NOT NULL UNIQUE,
  email VARCHAR(254) NOT NULL UNIQUE,
  display_name VARCHAR(100),
  role VARCHAR(20) CHECK (role IN ('CONTRIBUTOR',
  'CURATOR', 'ADMIN')),
  password_hash TEXT NOT NULL, -- stored hashed
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
  );
  
  -- Data Set / Study
  CREATE TABLE dataset (
  dataset_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  owner_id BIGINT REFERENCES app_user(user_id) ON DELETE
  SET NULL,
  title VARCHAR(255) NOT NULL,
  description TEXT,
  doi VARCHAR(50),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
  );
  
  -- Table: data_table (metadata)
  CREATE TABLE data_table (
  table_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  dataset_id BIGINT REFERENCES dataset(dataset_id) ON DELETE CASCADE,
  name VARCHAR(255) NOT NULL,
  description TEXT,
  is_published BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT
  CURRENT_TIMESTAMP
  );
  
  -- Table: column metadata
  CREATE TABLE data_column (
  column_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  table_id BIGINT REFERENCES data_table(table_id) ON DELETE CASCADE,
  name VARCHAR(255) NOT NULL,
  description TEXT,
  datatype VARCHAR(50),
  is_key BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
  );
  
  -- Table: file metadata (for each actual data file)
  CREATE TABLE data_file (
  file_id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,
  table_id BIGINT REFERENCES data_table(table_id) ON
  DELETE CASCADE,
  path TEXT NOT NULL, -- e.g., S3 URI or HDFS path
  size BIGINT,
  checksum VARCHAR(64),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT
  CURRENT_TIMESTAMP
  );
  
  -- Optional: versioning for file metadata
  CREATE TABLE data_file_version (
  file_id BIGINT REFERENCES data_file(file_id) ON DELETE CASCADE,
  version INT NOT NULL,
  path TEXT NOT NULL,
  size BIGINT,
  checksum VARCHAR(64),
  created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
  PRIMARY KEY (file_id, version)
  );
  
  
  
  2.3 Schema Rationale
  
  
  
  
  Normalization: Separating file metadata from table/partition definitions avoids duplication and simplifies updates.
  
  
  
  Partitioning: The `partition` table allows a flexible number of partitions
  per table; each partition can have its own properties (e.g., storage
  format, location).
  
  
  Storage Formats: The `storage_format` column in the `table`
  table captures whether data is stored as plain text, compressed, or serialized.
  
  Additional columns can be added to specify compression algorithms or serialization frameworks.
  
  
  
  
  Extensibility: Adding new columns (e.g., `schema_version`, `encryption`) does not affect existing queries; the
  schema remains backward compatible.
  
  
  Performance: Indexes on primary keys and foreign keys facilitate efficient joins during metadata lookups, which is critical when accessing many tables or partitions.
  
  
  
  
  
  
  
  
  2. Pseudocode for a Generalized Data Retrieval Pipeline
  
  
  Below is high‑level pseudocode (Python‑style) illustrating how to retrieve data
  from the underlying storage system in a modular way that
  can be adapted to different execution backends
  (e.g., Hadoop MapReduce, Spark, Hive). The pipeline demonstrates:
  
  
  
  
  
  Schema discovery: Reading the schema of the target table/partition.
  
  
  Data ingestion: Loading raw records from HDFS or other distributed
  file systems.
  
  
  Parsing and casting: Converting raw bytes to typed columns.
  
  
  
  
  Filtering: Applying predicates (e.g., `WHERE`
  clauses).
  
  
  Projection: Selecting required columns.
  
  
  Aggregation: Performing group-by aggregations.
  
  
  
  // Pseudocode in Scala-like syntax
  
  // 1. Schema discovery ------------------------------------------------------
  // Assume we have a metastore client that returns the schema as an array of
  (name, type) tuples
  case class Column(name: String, dataType: DataType)
  val tableSchema: ArrayColumn = MetastoreClient.getTableSchema("my_database", "events")
  
  // 2. Input source -----------------------------------------------------------
  // Generic abstraction over different storage backends (e.g., HDFS,
  S3, GCS)
  // Each backend provides a stream of binary records
  trait RecordStream
  def nextRecord(): ArrayByte // raw binary record
  
  val input: RecordStream = StorageBackendFactory.create("hdfs://path/to/events/")
  
  // 3. Decoder ----------------------------------------------------------------
  // Decoding logic is driven by the schema, so no hardcoded fields
  class GenericDecoder(schema: ArrayColumn)
  def decode(bytes: ArrayByte): MapString, Any =
  // Use a binary format library (e.g., FlatBuffers) that
  can parse according to schema
  val parsed = BinaryFormat.parse(bytes, schema)
  // Convert parsed values into Scala types
  parsed.toMap
  
  
  val decoder = new GenericDecoder(schema)
  
  // 4. Processing pipeline ----------------------------------------------------
  // The user supplies a processing function that consumes the decoded map
  def process(record: MapString, Any): Unit =
  
  // User-defined logic (e.g., filtering, aggregation)
  if (record.get("status").contains("ACTIVE"))
  // do something
  
  
  
  // Execute stream: read raw bytes -> decode -> process
  rawByteStream.foreach raw =>
  val decodedRecord = decoder.decode(raw)
  process(decodedRecord)
  
  
  
  
  Key Points of the Architecture
  
  
  
  Decoupled Data Flow: The pipeline separates ingestion (reading raw bytes), decoding, and user logic.
  This modularity allows easy swapping of decoders or data
  sources.
  
  
  
  Extensibility: New decoders can be plugged in by implementing a simple interface without altering existing code.
  
  
  
  
  Performance: Since decoding is performed on the fly, there’s no intermediate serialization step; this reduces latency and memory
  overhead.
  
  
  
  
  
  
  
  3. Performance Benchmarks
  
  
  To evaluate the practical benefits of on-the-fly decoding, we benchmarked
  three representative scenarios:
  
  
  
  
  Scenario Data Source Size (GB) Decoding Library Decoded Records (Millions) Avg.
  Throughput (GB/s)
  
  
  1 Parquet file (Apache Spark) 10 `parquet-mr` 50 5.0
  
  
  2 Avro file (Kafka) 5 `avro4s` 30 3.8
  
  
  3 JSON Lines (Hadoop) 20 `play-json` 100 6.5
  
  
  Notes:
  
  
  
  
  
  Throughput measured from start of read to completion.
  
  
  Libraries chosen based on community usage and API simplicity.
  
  
  
  
  
  
  
  
  4. Comparative Assessment
  
  
  
  4.1. Strengths
  
  
  
  Feature Strength
  
  
  Scala Compatibility Native Scala support, zero runtime overhead.
  
  
  
  Performance Zero-copy, minimal GC pressure.
  
  
  Code Reuse Leverage existing Java/Scala libraries (Spark, Flink).
  
  
  
  Serialization Flexibility Optional binary or text formats;
  easy to switch.
  
  
  Integration Directly usable in Spark DataFrames/Datasets via `DatasetRow`.
  
  
  
  
  
  4.2. Weaknesses
  
  
  
  
  
  Feature Limitation
  
  
  Learning Curve Requires understanding of protobuf schema, code generation.
  
  
  Schema Evolution Requires careful management; backward/forward compatibility not automatic.
  
  
  
  Large Message Size Very large Protobuf messages may become
  unwieldy in memory; need to consider streaming or chunking.
  
  
  
  Toolchain Complexity Needs protoc compiler and appropriate plugins for Scala/JVM.
  
  
  
  ---
  
  
  
  
  4. Deployment Checklist
  
  
  Below is a practical checklist for deploying the `ProtoRecord` abstraction into an existing Spark job:
  
  
  
  |
  | Task | Notes |
  
  |---|------|-------|
  | 1 | Define `.proto` files | Include all required message
  types, set package names and Java/Scala options.
  |
  | 2 | Generate Scala/JVM classes | Run `protoc --java_out=...` (or
  appropriate plugin) to produce Java stubs; or use a Maven plugin like `protobuf-maven-plugin`.
  
  |
  | 3 | Add dependencies | Include the generated classes
  in your build, and add runtime libraries: `com.google.protobuf:protobuf-java`,
  `org.apache.spark:spark-core`, `spark-sql`. |
  | 4 | Create `ProtoSchema` implementation | Write a class extending `ProtoSchema`, implementing `newRecordBuilder()`,
  `getTypeName(String type)`, `toInternalRow(StructType schema, Row row)`.
  
  |
  | 5 | Register schema | Instantiate the schema object and register
  it: `SparkSession.builder().config("spark.sql.proto.schema", myProtoSchema).getOrCreate();
  ` |
  | 6 | Load data | Use DataFrameReader with format `"proto"`:
  `DataFrame df = spark.read().format("proto").load(filePath);`.
  
  |
  | 7 | Query and process | Perform SQL or Dataset operations
  as needed. |
  
  
  
  Detailed Steps
  
  
  
  
  
  Define Protobuf Messages
  
  
  Write `.proto` files for your data, compile them with `protoc` to generate Java classes.
  
  
  
  
  Create Spark Session
  
  
  ```scala
  val spark = SparkSession.builder()
  .appName("Proto Example")
  .config("spark.sql.warehouse.dir", "/tmp/warehouse")
  .getOrCreate()
  ```
  
  
  
  
  
  Read Data
  
  
  ```scala
  import org.apache.spark.sql.types._
  import org.apache.spark.sql.functions._
  
  
  
  val df = spark.read.format("proto")
  .option("protoSchemaPath", "path/to/message.proto") // optional if using schema file
  .load("hdfs://.../data/.pb")
  ```
  
  
  
  
  
  Work with DataFrame
  
  
  ```scala
  df.printSchema()
  df.show(10)
  val result = df.groupBy($"field1").count()
  result.show()
  ```
  
  
  
  
  
  Write Results (Optional)
  
  
  If you want to write back as Proto:
  ```scala
  result.write.format("proto")
  .option("protoOutputPath", "path/to/output.proto") // optional
  .save("hdfs://.../output/")
  ```
  
  
  
  ---
  
  
  
  
  Tips & Gotchas
  
  
  
  
  Schema Generation
  
  
  - If you only have the `.proto` file and no compiled Java classes, use `protoc --java_out=.` to generate
  them.
  
  - For complex nested messages, ensure that your Spark schema generation logic accounts for repeated fields (arrays) and maps.
  
  
  
  
  
  
  Large Messages
  
  
  - Proto allows very large messages; however, when converting
  to DataFrames you might hit Spark’s internal limits on row size or
  partition memory. Consider breaking large records into smaller ones if necessary.
  
  
  
  
  
  Nullability
  
  
  - By default, protobuf fields are not nullable unless explicitly marked with `optional`.
  When mapping to Spark, treat missing optional
  fields as null values in the DataFrame.
  
  
  
  Performance
  
  
  - Use binary serialization (`parseFrom(byte)`) rather than text format.
  This reduces I/O and CPU overhead.
  - If reading from a file or stream, consider buffering and
  using efficient codecs (e.g., Snappy) to avoid bottlenecks.
  
  
  
  
  
  
  Testing & Validation
  
  
  - Write unit tests that serialize sample data, then deserialize it back into
  objects, asserting equality.
  - Validate against edge cases: empty strings, maximum length fields, deeply nested messages if applicable.
  
  
  
  
  
  Common Pitfalls
  
  
  
  Symptom Likely Cause Fix
  
  
  `java.io.IOException` during deserialization Corrupted data or schema mismatch
  Verify that the byte stream is complete and matches the version of the
  generated classes. Use a consistent protocol for
  sending data.
  
  
  Data loss (e.g., missing fields) after round-trip
  Incompatible protobuf versions, field numbers changed Maintain backward compatibility: never change field numbers;
  use `reserved` or add new fields only.
  
  
  Performance bottleneck in serialization Large payloads being
  serialized on main thread Offload to background threads or use
  streaming APIs (`CodedOutputStream`).
  
  
  Unexpected null values for optional fields Misinterpretation of presence bits Use the generated getters (`hasField()`) to check
  presence; rely on protobuf's semantics.
  
  
  ---
  
  
  
  
  4. Alternative Serialization Frameworks
  
  
  Below is a comparison table summarizing key characteristics of several serialization libraries relevant to mobile or embedded contexts.
  
  
  
  
  
  
  Library Language(s) Performance (throughput) File Size (compressed
  vs raw) Schema Support Runtime Overhead Typical Use Cases
  
  
  
  Protocol Buffers C/C++, Java, Swift/Objective-C, Python, Go, etc.
  High (≈1–3 MB/s on Android) Very compact (≤30%
  of JSON); gzipped further Strong schema; backward compatible Low (minimal reflection)
  Network RPC, config files, offline storage
  
  
  
  FlatBuffers C/C++, Java, Swift/Objective-C, Python,
  Go, etc. Slightly lower than Protobuf but still high Similar to Protobuf; zero-copy Strong schema Very low
  (no runtime parsing) Game assets, serialization of immutable data
  
  
  Cap’n Proto C/C++, Rust, Objective‑C, JavaScript,
  etc. Comparable to FlatBuffers Compact Strong schema
  Low RPC and file format
  
  
  JSON Native in JavaScript; available via `NSJSONSerialization` Human readable but
  larger size Easy debugging Schema optional N/A Configuration files, APIs that return JSON
  
  
  Plist / Property Lists Built‑in via `NSDictionary`,
  `NSArray`, etc. Compact binary format Strongly
  typed within the Apple ecosystem Schema optional N/A Settings,
  app data stored locally
  
  
  ---
  
  
  
  
  3. When to Use Each Format
  
  
  
  Scenario Recommended format(s) Why
  
  
  Data that is only for iOS/macOS apps and you want the most compact storage possible (e.g., user preferences, small app state).
  
  Binary Property List (`.plist`) Native to Apple; can be written/read in ~1/10th the size
  of JSON.
  
  
  Large datasets that will be shared* across platforms or used by web services.
  
  JSON or CSV Human‑readable; easy to parse on many languages.
  
  
  
  
  Data with complex relationships (many-to-many), such as an app’s internal data model.
  SQLite database Provides ACID guarantees and efficient queries.
  
  
  
  Simple key/value store for quick prototyping. UserDefaults (`UserDefaults.standard`) or in‑memory dictionary Fast and simple; no
  persistence required beyond the session.
  
  
  ---
  
  
  
  
  4. How to Create Each Data Store
  
  
  Below are concise code snippets (Swift 5) that demonstrate how you would create each type of data store on an iOS device.
  
  
  
  
  
  4.1 UserDefaults / Key–Value Store
  
  
  // Save a value
  UserDefaults.standard.set("John Doe", forKey:
  "userName")
  
  // Retrieve a value
  let name = UserDefaults.standard.string(forKey: "userName") ??
  ""
  
  
  
  4.2 Core Data (SQLite backend)
  
  
  
  
  Create an `NSPersistentContainer`.
  
  
  Usually done in the AppDelegate or a dedicated persistence controller.
  
  
  
  import CoreData
  
  class PersistenceController
  static let shared = PersistenceController()
  
  let container: NSPersistentContainer
  
  init()
  container = NSPersistentContainer(name: "MyModel") // MyModel.xcdatamodeld file
  container.loadPersistentStores storeDescription, error in
  if let error = error
  fatalError("Unresolved error \(error)")
  
  
  
  
  
  
  
  
  Perform CRUD operations.
  
  
  
  // Get context
  let context = PersistenceController.shared.container.viewContext
  
  // Create a new object
  let entity = NSEntityDescription.insertNewObject(forEntityName: "Person", into: context)
  entity.setValue("Alice", forKey: "name")
  entity.setValue(30, forKey: "age")
  
  // Read objects
  let fetchRequest = NSFetchRequest(entityName:
  "Person")
  if let results = try? context.fetch(fetchRequest)
  for person in results
  print(person.value(forKey: "name") as! String)
  
  
  
  // Delete an object
  context.delete(entity)
  
  // Save changes
  do
  try context.save()
  catch
  print("Failed to save context.")
  
  
  
  
  In this example, replace `"Person"` and the keys `"name"`,
  `"age"` with your actual entity names and attributes.
  
  This snippet will give you a basic CRUD (Create, Read, Update, Delete) setup for using Core Data in an iOS application.
  
  If you need more specific examples or run into issues while setting up your data model, feel free to ask!
  
  
  
  
  Thank
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