Reddit Data Export Formats and Schema Design

Choose optimal formats for Reddit data storage, analysis, and interoperability

22 min read

Intermediate

Updated Feb 2026

Selecting the right data format for Reddit exports impacts storage costs, query performance, and downstream compatibility. This guide covers format selection, schema design, and best practices for Reddit data at any scale.

Format Comparison

Format	Compression	Query Speed	Human Readable	Best For
JSON Lines	Medium	Slow	Yes	Streaming, debugging
CSV	Medium	Slow	Yes	Spreadsheets, simple analysis
Parquet	Excellent	Fast	No	Analytics, data lakes
Avro	Good	Fast	No	Streaming pipelines

JSON Lines (JSONL) Common

One JSON object per line. Easy to stream, append, and debug.

Use for: Real-time ingestion, debugging, small datasets
Avoid for: Large-scale analytics, frequent queries

Apache Parquet Recommended

Columnar format optimized for analytical queries. Industry standard for data lakes.

Use for: Data warehouses, Spark/Pandas analysis, long-term storage
Avoid for: Real-time streaming, simple scripts

JSON / JSONL Format

JSON Lines is the most common format for Reddit data exchange due to its simplicity and Reddit API's native JSON responses.

                        jsonl - reddit_posts.jsonl
                    
{"id": "1abc123", "subreddit": "technology", "title": "New AI breakthrough announced", "selftext": "...", "author": "user123", "score": 1542, "num_comments": 234, "created_utc": 1706745600}
{"id": "1abc124", "subreddit": "programming", "title": "Best practices for API design", "selftext": "...", "author": "dev_user", "score": 892, "num_comments": 156, "created_utc": 1706745700}
{"id": "1abc125", "subreddit": "startups", "title": "How we scaled to 1M users", "selftext": "...", "author": "founder", "score": 2341, "num_comments": 445, "created_utc": 1706745800}

                        python - jsonl_export.py
                    

import json
from typing import List, Dict, Iterator
import gzip

class JSONLExporter:
    """Export Reddit data to JSON Lines format."""

    def __init__(self, compress: bool = True):
        self.compress = compress

    def export(
        self,
        posts: List[Dict],
        filepath: str
    ):
        """Export posts to JSONL file."""
        if self.compress:
            filepath = f"{filepath}.gz"
            open_func = gzip.open
            mode = 'wt'
        else:
            open_func = open
            mode = 'w'

        with open_func(filepath, mode, encoding='utf-8') as f:
            for post in posts:
                f.write(json.dumps(post, ensure_ascii=False) + '\n')

    def stream_export(
        self,
        posts: Iterator[Dict],
        filepath: str,
        buffer_size: int = 1000
    ):
        """Stream export for large datasets."""
        open_func = gzip.open if self.compress else open
        mode = 'wt' if self.compress else 'w'

        buffer = []
        with open_func(filepath, mode, encoding='utf-8') as f:
            for post in posts:
                buffer.append(json.dumps(post, ensure_ascii=False))

                if len(buffer) >= buffer_size:
                    f.write('\n'.join(buffer) + '\n')
                    buffer = []

            if buffer:
                f.write('\n'.join(buffer) + '\n')


def read_jsonl(filepath: str) -> Iterator[Dict]:
    """Read JSONL file, handling compression."""
    open_func = gzip.open if filepath.endswith('.gz') else open
    mode = 'rt' if filepath.endswith('.gz') else 'r'

    with open_func(filepath, mode, encoding='utf-8') as f:
        for line in f:
            yield json.loads(line)


# Usage
exporter = JSONLExporter(compress=True)
exporter.export(posts, 'reddit_export.jsonl')
                    

Apache Parquet Format

Parquet is the recommended format for analytical workloads. Its columnar storage enables fast queries and excellent compression.

Why Parquet for Reddit Data?

Parquet achieves 10-20x compression on Reddit text data and enables predicate pushdown for filtered queries. A 1GB JSON export typically compresses to 50-100MB in Parquet.

                        python - parquet_export.py
                    

import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
from typing import List, Dict

class ParquetExporter:
    """Export Reddit data to Parquet format."""

    # Define schema for type consistency
    SCHEMA = pa.schema([
        ('id', pa.string()),
        ('subreddit', pa.string()),
        ('title', pa.string()),
        ('selftext', pa.string()),
        ('author', pa.string()),
        ('score', pa.int32()),
        ('num_comments', pa.int32()),
        ('created_utc', pa.timestamp('s')),
        ('url', pa.string()),
        ('permalink', pa.string()),
        ('is_self', pa.bool_()),
        ('over_18', pa.bool_()),

        # Enriched fields
        ('sentiment_label', pa.string()),
        ('sentiment_score', pa.float32()),
        ('categories', pa.list_(pa.string())),
        ('entities', pa.map_(pa.string(), pa.list_(pa.string()))),
    ])

    def __init__(
        self,
        compression: str = 'snappy',
        row_group_size: int = 100000
    ):
        self.compression = compression
        self.row_group_size = row_group_size

    def export(
        self,
        posts: List[Dict],
        filepath: str
    ):
        """Export posts to Parquet file."""
        # Convert to DataFrame
        df = pd.DataFrame(posts)

        # Ensure timestamp conversion
        if 'created_utc' in df.columns:
            df['created_utc'] = pd.to_datetime(
                df['created_utc'],
                unit='s'
            )

        # Write to Parquet
        table = pa.Table.from_pandas(df)
        pq.write_table(
            table,
            filepath,
            compression=self.compression,
            row_group_size=self.row_group_size
        )

    def export_partitioned(
        self,
        posts: List[Dict],
        base_path: str,
        partition_cols: List[str] = None
    ):
        """
        Export with partitioning for efficient querying.

        Recommended partitions: year, month, subreddit
        """
        partition_cols = partition_cols or ['year', 'month']

        df = pd.DataFrame(posts)

        # Add partition columns
        if 'created_utc' in df.columns:
            df['created_utc'] = pd.to_datetime(df['created_utc'], unit='s')
            df['year'] = df['created_utc'].dt.year
            df['month'] = df['created_utc'].dt.month

        table = pa.Table.from_pandas(df)

        pq.write_to_dataset(
            table,
            root_path=base_path,
            partition_cols=partition_cols,
            compression=self.compression
        )


def read_parquet(
    filepath: str,
    columns: List[str] = None,
    filters = None
) -> pd.DataFrame:
    """
    Read Parquet with optional column selection and filtering.

    filters example: [('subreddit', '=', 'technology')]
    """
    return pq.read_table(
        filepath,
        columns=columns,
        filters=filters
    ).to_pandas()


# Usage
exporter = ParquetExporter(compression='zstd')  # Best compression
exporter.export(posts, 'reddit_export.parquet')

# Read with filtering
df = read_parquet(
    'reddit_export.parquet',
    columns=['id', 'title', 'score'],
    filters=[('score', '>', 100)]
)
                    

Reddit Post Schema

A well-designed schema ensures data consistency and enables efficient querying. Here is the recommended schema for Reddit posts.

                        python - schemas.py
                    

from dataclasses import dataclass, field
from typing import List, Dict, Optional
from datetime import datetime

@dataclass
class RedditPostSchema:
    """
    Standard schema for Reddit post exports.

    Core fields from Reddit API plus enrichments.
    """

    # Core identifiers
    id: str                          # Reddit post ID (e.g., "1abc123")
    subreddit: str                   # Subreddit name without r/
    subreddit_id: str                # Full subreddit ID (e.g., "t5_2qh33")

    # Content
    title: str                       # Post title
    selftext: str                    # Post body (empty for link posts)
    selftext_html: Optional[str] = None  # HTML-formatted body

    # Author info
    author: str                      # Username (without u/)
    author_fullname: Optional[str] = None  # Full author ID

    # Engagement metrics
    score: int                       # Net upvotes (can be negative)
    upvote_ratio: float              # Ratio of upvotes (0-1)
    num_comments: int                # Comment count
    num_crossposts: int = 0          # Times crossposted

    # Timestamps
    created_utc: datetime            # Creation time (UTC)
    edited: Optional[datetime] = None  # Last edit time

    # URLs and links
    url: str                         # Linked URL or self post URL
    permalink: str                   # Reddit permalink
    domain: str                      # Link domain (self.subreddit for text)

    # Post type flags
    is_self: bool                    # True for text posts
    is_video: bool = False
    is_original_content: bool = False
    over_18: bool = False            # NSFW flag
    spoiler: bool = False
    stickied: bool = False

    # Flair
    link_flair_text: Optional[str] = None
    link_flair_css_class: Optional[str] = None

    # Awards
    total_awards_received: int = 0

    # Enriched fields (added by processing)
    sentiment_label: Optional[str] = None      # positive/neutral/negative
    sentiment_score: Optional[float] = None   # Confidence (0-1)
    categories: List[str] = field(default_factory=list)
    keywords: List[str] = field(default_factory=list)
    entities: Dict[str, List[str]] = field(default_factory=dict)

    # Ingestion metadata
    ingested_at: datetime = field(default_factory=datetime.utcnow)
    source: str = 'reddit_api'      # Data source identifier
    schema_version: str = '1.0.0'   # Schema version for evolution
                    

Export Reddit Data Instantly

reddapi.dev provides one-click export to CSV, JSON, and Excel. Get structured data with sentiment and entity enrichment included.

Try Data Export

Compression Strategies

Compression reduces storage costs and can improve query performance by reducing I/O.

Algorithm	Compression Ratio	Speed	CPU Usage	Best For
Snappy	Medium (~3x)	Very Fast	Low	Real-time, frequent reads
Gzip	High (~6x)	Slow	High	Archival, infrequent reads
Zstd	High (~7x)	Fast	Medium	Best balance (recommended)
LZ4	Low (~2x)	Fastest	Very Low	Streaming, low latency

Data Partitioning

Partitioning enables efficient querying of large datasets by organizing data into directories based on common filter columns.

Recommended Partition Strategy

For Reddit data, partition by year/month for time-based queries, or by subreddit for community-focused analysis. Avoid over-partitioning (too many small files) - aim for partition sizes of 100MB-1GB.

                        text - partition_structure
                    

# Time-based partitioning (recommended for analytics)
reddit_data/
├── year=2026/
│   ├── month=01/
│   │   ├── part-00000.parquet
│   │   └── part-00001.parquet
│   └── month=02/
│       └── part-00000.parquet
└── _SUCCESS

# Subreddit-based partitioning (for community analysis)
reddit_data/
├── subreddit=technology/
│   └── data.parquet
├── subreddit=programming/
│   └── data.parquet
└── subreddit=startups/
    └── data.parquet

# Hybrid partitioning (for large-scale systems)
reddit_data/
├── year=2026/
│   └── month=01/
│       ├── subreddit=technology/
│       │   └── data.parquet
│       └── subreddit=programming/
│           └── data.parquet
└── _SUCCESS
                    

Frequently Asked Questions

Which format should I use for Reddit data exports?

Use JSONL for small datasets, streaming pipelines, and debugging. Use Parquet for analytics, data lakes, and long-term storage. CSV works for simple spreadsheet analysis but loses nested data (entities, lists). For most production use cases, Parquet with Zstd compression provides the best balance of size, speed, and compatibility.

How do I handle nested data like entities in CSV format?

CSV does not natively support nested data. Options include: (1) Flatten nested fields into separate columns (entities_ORG, entities_PERSON), (2) JSON-encode nested fields as strings, (3) Create separate CSVs for nested data with foreign key relationships, or (4) Use Parquet which natively supports nested structures.

What compression ratio can I expect for Reddit text data?

Reddit text compresses very well due to repetitive vocabulary. Expect 5-10x compression with Gzip/Zstd on JSONL, and 10-20x with Parquet (which combines columnar storage with compression). A 10GB raw JSON export typically compresses to 500MB-1GB in Parquet with Zstd.

How should I handle schema changes over time?

Include a schema_version field in your data. Use backward-compatible changes: add new optional fields rather than modifying existing ones. For Parquet, schema evolution is handled automatically for added nullable columns. Maintain schema documentation and validate data against schemas during ingestion.

What is the maximum recommended file size for Parquet?

Aim for 100MB-1GB per file for optimal query performance. Smaller files increase metadata overhead; larger files reduce parallelism. For partitioned datasets, ensure each partition directory contains reasonably-sized files. Use row group sizes of 50,000-100,000 rows for efficient compression and filtering.