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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
Before any file is transmitted, DSpace verifies that the requesting user 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 login page or provide an error message indicating insufficient privileges.
2. Logging and Auditing
Each download request is recorded in server logs for compliance purposes. The log entry typically includes:
User identifier
Timestamp of the request
Resource requested (e.g., `article_12345.pdf`)
Outcome (success, failure)
These records help maintain an audit trail and can be used to analyze usage patterns or investigate potential security incidents.
3. Content Delivery
Once authorization is confirmed, the server retrieves the requested file from persistent storage. Depending on configuration:
Direct Streaming: The server streams the binary data directly to the client’s browser.
Download Prompt: An HTTP `Content-Disposition` header may trigger a "Save As" dialog.
Large files might be served via a Content Delivery Network (CDN) or through range requests that allow resuming interrupted downloads.
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 - https://graph.org/Ultimate-Stack-Seven-Prime-Testosterone-Cycles-to-Maximize-Gains-10-02
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