Douyin / TikTok Video ID Decoder (Snowflake-style)

中文 | English

English Documentation · 2025 Edition · Validated with 73 Real Data Samples

Decode aweme_id (64-bit unsigned integer) to extract publish timestamp (second-precision) and analyze the low 32 bits for time-series research, sharding/QPS inference, and risk control analysis. This tool has been validated with 73 real data samples (53 from Douyin / 20 from TikTok).

🌐 Online Decoder

No installation required, use instantly: https://evil0ctal.github.io/Douyin-TikTok-Video-ID-Decoder/

✨ Features

🚀 Completely Free: No registration required, ready to use
🌍 Bilingual Support: Auto-detects browser language, manual switching available
📱 Responsive Design: Perfect for desktop and mobile devices
🎨 Dark Mode: Automatically adapts to system theme
⚡ Batch Decoding: Decode multiple video IDs simultaneously
🔐 Privacy First: All decoding happens locally in your browser, no data upload

📸 Interface Preview

English Interface:

🎯 How to Use

Visit the Online Decoder
Paste one or more video IDs in the input box (one per line)
Click the "Decode" button to view results
Results include: UTC time, local time, timestamp, shard ID, sequence number, and more

Shortcut: Press Ctrl + Enter for quick decoding

1) TL;DR Summary

Bit Structure
- High 32 bits = Unix Timestamp (seconds)
- Low 32 bits = Uniqueness bits (suspected "shard/machine + sequence", exact bit-width not publicly disclosed)
Accuracy (≤5 seconds as "close match")
- Validation samples: 73 total (53 Douyin / 20 TikTok)
- Exact match: 0%
- ≤5 seconds error: 26.0%
- Average absolute error: 14.3 seconds (max 49s, min 2s)
- Error direction: Negative bias (decoded time is earlier than actual):
  - Douyin average error: -12.98 seconds (range -49 ~ -2)
  - TikTok average error: -17.80 seconds (range -31 ~ -7)
Interpretation
- High 32 bits do carry second-level time information, but with consistent small deviation from platform-returned create_time (typically -7 ~ -30 seconds), likely due to reporting/storage timing, server aggregation delay, rounding/sampling, or different field semantics.
- Engineering recommendation: Apply platform-specific second-level calibration (see §5) to significantly improve alignment with your data source's create_time.

2) Core Features

🔍 Basic Decoding

Extract Unix timestamp (second-precision)
Output UTC and local timezone datetime
Display low 32 bits in multiple formats (decimal/hexadecimal/binary)

🧬 Low 32-bit Multi-dimensional Analysis

Scheme	Bit Division	Description
Scheme 1	`10+10+12`	Standard Snowflake division (datacenter+worker+sequence)
Scheme 2	`8+8+16`	Modified version (larger sequence space)
Scheme 3	`16+16`	Recommended (convenient for shard/sequence clustering)
Scheme 4	`8+8+8+8`	Byte-level analysis

📊 Statistical Analysis Features

✅ Sequence number duplication detection (identify batch uploads)
✅ Shard ID distribution statistics
✅ Time distribution analysis (time span, earliest/latest)
✅ Low byte pattern recognition
✅ Platform comparison (Douyin vs TikTok)

✨ Advanced Features

🎯 Algorithm Accuracy Validation: Automatically validate decoding algorithm with real data
📈 Visualization: ASCII graphical display of 64-bit structure
🔧 Reverse Construction: Generate Snowflake-like IDs for testing

3) Key Findings from Validation

Systematic Negative Bias:
- Douyin: average -12.98s
- TikTok: average -17.80s
- Both platforms show consistent negative bias, indicating "ID timestamp" is typically earlier than your sample's create_time field.
Error Distribution: Most errors fall in -7 ~ -30s range, with extremes up to -49s.
Low-bit Patterns & Duplicates:
- Example: sequence 0x0d23 (3363) appears in two Douyin samples; 0x4d1e (19742) in TikTok samples.
- Byte0 shows high frequency of 0x1e in TikTok; 0x23 in Douyin (small sample size, indicative only).
- These signals support the "low bits contain sequence/shard" engineering hypothesis, but exact bit-widths require larger sample validation.

4) Understanding the "Error"

Why isn't it 100% accurate to the second?

Field Semantic Difference: Platform-returned create_time might be "content visible/online" time or "aggregation/storage completion" time, while ID timestamp is closer to "generation/allocation" moment.
Pipeline & Caching: Asynchronous operations and retries exist across multi-tier services (upload, review, transcoding, risk control, distribution, indexing).
Rounding/Sampling: Internal second-level alignment/rounding can cause +/− several seconds.
Timezone Independent: UTC/LA time displayed are calculated from the timestamp; not a timezone display issue.

Conclusion: ID's high 32 bits are sufficient for "time sorting, range filtering, approximate positioning". For strict alignment with platform create_time for "precise alerting/tracing", apply platform-level calibration (next section).

5) Practical Recommendation: Second-level Calibration

To better align with create_time, apply platform-specific fixed calibration values (configurable):

Recommended initial values:
- offset_seconds_douyin = +13
- offset_seconds_tiktok = +18
Usage: After decoding, timestamp_sec += offset_seconds_* before comparison/display.
Expected effect:
- Pull "negative bias" back to 0 ~ +5 second range
- In practice, significantly improves "≤5 seconds error" match rate (actual effect depends on your sample source)
Operationalization:
- Make calibration values configurable (ENV or CLI parameters), support A/B adjustment
- Periodically re-evaluate with recent samples, auto-tune (e.g., use offset mean of recent 10k samples as calibration)

Note: Keep both "raw decoded timestamp" and "calibrated timestamp" fields for audit and tracing.

6) Research Roadmap for Low 32 Bits (For Engineers)

Same-second Sampling: Collect as many aweme_ids from the same second, observe low-bit increment and wrap-around under different schemes
Fit Sequence Bit-width: Estimate "sequence number bits" from wrap-around points (e.g., 0→N→0 pattern)
Shard Clustering: Remaining high bits can be treated as "shard/machine", cluster and analyze QPS, geographic/IDC distribution per shard
Cross-platform Comparison: Compare Douyin vs TikTok low-bit distributions to see if they're isomorphic, reducing false positives

Current sample size is limited; 16+16 scheme is more convenient for quick clustering and visualization, but doesn't claim to be the true bit-width.

7) Quick Start

📦 Requirements

Python 3.9+
Dependencies: None (standard library only)

🚀 Basic Usage

Method 1: Run Full Demo

python decode_aweme_id.py

Automatically executes:

✅ Algorithm Validation (using aweme_ids_output.json)
📋 Basic decoding demo
🔬 Low 32-bit deep analysis
📊 Statistical analysis and pattern recognition
🎨 Bit structure visualization
🔧 Reverse construction example

Method 2: Validation Only

python test_validation.py

Method 3: Use in Code

from decode_aweme_id import decode_aweme_id, validate_decode_algorithm

# Decode single ID
result = decode_aweme_id("7350810998023949599")
print(result['datetime_utc'])  # Output UTC time

# Run full validation
validate_decode_algorithm()

📄 Data Format

Input file: aweme_ids_output.json

{
  "total_count": 73,
  "douyin_count": 53,
  "tiktok_count": 20,
  "videos": [
    {
      "aweme_id": "7350810998023949599",
      "create_time": 1711494099,
      "create_datetime": "2024-03-26 19:34:59",
      "source": "Douyin"
    }
  ]
}

📤 Output Contents

After running the script, you'll get:

Module	Description
Algorithm Validation	Accuracy stats, error analysis, platform comparison
Basic Decoding	Timestamp, UTC/local time, low 32-bit representations
Low-bit Analysis	Detailed breakdown using 4 schemes
Statistical Analysis	Sequence duplicates, shard distribution, time span
Visualization	64-bit binary structure ASCII diagram
Construction Test	Reverse generate ID and verify

Partial output:

[Douyin] 7153549929326120227
  实际时间 / Actual time: 2022-10-12 02:07:20 (ts: 1665565640)
  解码时间 / Decoded time: 2022-10-12T09:07:14+00:00 (ts: 1665565634)
  ✗ 误差 / Error (-6秒 / seconds)

[Douyin] 7266740902494833931
  实际时间 / Actual time: 2023-08-13 02:46:09 (ts: 1691919969)
  解码时间 / Decoded time: 2023-08-13T09:46:01+00:00 (ts: 1691919961)
  ✗ 误差 / Error (-8秒 / seconds)

[Douyin] 7553588363032005946
  实际时间 / Actual time: 2025-09-24 02:40:46 (ts: 1758706846)
  解码时间 / Decoded time: 2025-09-24T09:40:40+00:00 (ts: 1758706840)
  ✗ 误差 / Error (-6秒 / seconds)

[Douyin] 7542102211314076986
  实际时间 / Actual time: 2025-08-24 03:48:38 (ts: 1756032518)
  解码时间 / Decoded time: 2025-08-24T10:48:32+00:00 (ts: 1756032512)
  ✗ 误差 / Error (-6秒 / seconds)

[Douyin] 7506081353857207609
  实际时间 / Actual time: 2025-05-19 02:09:18 (ts: 1747645758)
  解码时间 / Decoded time: 2025-05-19T09:09:12+00:00 (ts: 1747645752)
  ✗ 误差 / Error (-6秒 / seconds)

[Douyin] 7502379797902216484
  实际时间 / Actual time: 2025-05-09 02:45:28 (ts: 1746783928)
  解码时间 / Decoded time: 2025-05-09T09:45:17+00:00 (ts: 1746783917)
  ✗ 误差 / Error (-11秒 / seconds)

[Douyin] 7502000723766430988
  实际时间 / Actual time: 2025-05-08 02:14:29 (ts: 1746695669)
  解码时间 / Decoded time: 2025-05-08T09:14:17+00:00 (ts: 1746695657)
  ✗ 误差 / Error (-12秒 / seconds)

[Douyin] 7490506246395563300
  实际时间 / Actual time: 2025-04-07 02:50:12 (ts: 1744019412)
  解码时间 / Decoded time: 2025-04-07T09:49:50+00:00 (ts: 1744019390)
  ✗ 误差 / Error (-22秒 / seconds)

[Douyin] 7488278337312263460
  实际时间 / Actual time: 2025-04-01 02:44:35 (ts: 1743500675)
  解码时间 / Decoded time: 2025-04-01T09:44:25+00:00 (ts: 1743500665)
  ✗ 误差 / Error (-10秒 / seconds)

[Douyin] 7484211357902966079
  实际时间 / Actual time: 2025-03-21 03:42:43 (ts: 1742553763)
  解码时间 / Decoded time: 2025-03-21T10:42:27+00:00 (ts: 1742553747)
  ✗ 误差 / Error (-16秒 / seconds)

[Douyin] 7477512161175080255
  实际时间 / Actual time: 2025-03-03 01:26:23 (ts: 1740993983)
  解码时间 / Decoded time: 2025-03-03T09:26:09+00:00 (ts: 1740993969)
  ✗ 误差 / Error (-14秒 / seconds)

[Douyin] 7476417322140437796
  实际时间 / Actual time: 2025-02-28 02:37:56 (ts: 1740739076)
  解码时间 / Decoded time: 2025-02-28T10:37:37+00:00 (ts: 1740739057)
  ✗ 误差 / Error (-19秒 / seconds)

[Douyin] 7475679294484729124
  实际时间 / Actual time: 2025-02-26 02:54:05 (ts: 1740567245)
  解码时间 / Decoded time: 2025-02-26T10:53:41+00:00 (ts: 1740567221)
  ✗ 误差 / Error (-24秒 / seconds)

[Douyin] 7473824079032749323
  实际时间 / Actual time: 2025-02-21 02:54:41 (ts: 1740135281)
  解码时间 / Decoded time: 2025-02-21T10:54:30+00:00 (ts: 1740135270)
  ✗ 误差 / Error (-11秒 / seconds)

[Douyin] 7473079802631589177
  实际时间 / Actual time: 2025-02-19 02:46:29 (ts: 1739961989)
  解码时间 / Decoded time: 2025-02-19T10:46:20+00:00 (ts: 1739961980)
  ✗ 误差 / Error (-9秒 / seconds)

[Douyin] 7471222653760654611
  实际时间 / Actual time: 2025-02-14 02:39:59 (ts: 1739529599)
  解码时间 / Decoded time: 2025-02-14T10:39:39+00:00 (ts: 1739529579)
  ✗ 误差 / Error (-20秒 / seconds)

[Douyin] 7468236778898459943
  实际时间 / Actual time: 2025-02-06 01:33:20 (ts: 1738834400)
  解码时间 / Decoded time: 2025-02-06T09:32:56+00:00 (ts: 1738834376)
  ✗ 误差 / Error (-24秒 / seconds)

[Douyin] 7467859527912770870
  实际时间 / Actual time: 2025-02-05 01:09:35 (ts: 1738746575)
  解码时间 / Decoded time: 2025-02-05T09:09:00+00:00 (ts: 1738746540)
  ✗ 误差 / Error (-35秒 / seconds)

[Douyin] 7464176276731890983
  实际时间 / Actual time: 2025-01-26 02:56:29 (ts: 1737888989)
  解码时间 / Decoded time: 2025-01-26T10:56:06+00:00 (ts: 1737888966)
  ✗ 误差 / Error (-23秒 / seconds)

[Douyin] 7461944739479194899
  实际时间 / Actual time: 2025-01-20 02:37:09 (ts: 1737369429)
  解码时间 / Decoded time: 2025-01-20T10:36:36+00:00 (ts: 1737369396)
  ✗ 误差 / Error (-33秒 / seconds)

[Douyin] 7455641451033005324
  实际时间 / Actual time: 2025-01-03 02:57:08 (ts: 1735901828)
  解码时间 / Decoded time: 2025-01-03T10:56:37+00:00 (ts: 1735901797)
  ✗ 误差 / Error (-31秒 / seconds)

[Douyin] 7454518969194630412
  实际时间 / Actual time: 2024-12-31 02:21:20 (ts: 1735640480)
  解码时间 / Decoded time: 2024-12-31T10:20:49+00:00 (ts: 1735640449)
  ✗ 误差 / Error (-31秒 / seconds)

[Douyin] 7454129794217790774
  实际时间 / Actual time: 2024-12-30 01:11:25 (ts: 1735549885)
  解码时间 / Decoded time: 2024-12-30T09:10:37+00:00 (ts: 1735549837)
  ✗ 误差 / Error (-48秒 / seconds)

[Douyin] 7453025153564298559
  实际时间 / Actual time: 2024-12-27 01:44:16 (ts: 1735292656)
  解码时间 / Decoded time: 2024-12-27T09:44:03+00:00 (ts: 1735292643)
  ✗ 误差 / Error (-13秒 / seconds)

[Douyin] 7451536968590150923
  实际时间 / Actual time: 2024-12-23 01:29:57 (ts: 1734946197)
  解码时间 / Decoded time: 2024-12-23T09:29:08+00:00 (ts: 1734946148)
  ✗ 误差 / Error (-49秒 / seconds)

[Douyin] 7449329569653460282
  实际时间 / Actual time: 2024-12-17 02:43:28 (ts: 1734432208)
  解码时间 / Decoded time: 2024-12-17T10:43:18+00:00 (ts: 1734432198)
  ✗ 误差 / Error (-10秒 / seconds)

[Douyin] 7444133391118683442
  实际时间 / Actual time: 2024-12-03 02:39:50 (ts: 1733222390)
  解码时间 / Decoded time: 2024-12-03T10:39:28+00:00 (ts: 1733222368)
  ✗ 误差 / Error (-22秒 / seconds)

[Douyin] 7443366116870720777
  实际时间 / Actual time: 2024-12-01 01:02:24 (ts: 1733043744)
  解码时间 / Decoded time: 2024-12-01T09:02:03+00:00 (ts: 1733043723)
  ✗ 误差 / Error (-21秒 / seconds)

[Douyin] 7441195561165851914
  实际时间 / Actual time: 2024-11-25 04:39:22 (ts: 1732538362)
  解码时间 / Decoded time: 2024-11-25T12:39:12+00:00 (ts: 1732538352)
  ✗ 误差 / Error (-10秒 / seconds)

[Douyin] 7432607934867180809
  实际时间 / Actual time: 2024-11-02 02:15:06 (ts: 1730538906)
  解码时间 / Decoded time: 2024-11-02T09:14:49+00:00 (ts: 1730538889)
  ✗ 误差 / Error (-17秒 / seconds)

[Douyin] 7426764196366339354
  实际时间 / Actual time: 2024-10-17 08:18:21 (ts: 1729178301)
  解码时间 / Decoded time: 2024-10-17T15:18:08+00:00 (ts: 1729178288)
  ✗ 误差 / Error (-13秒 / seconds)

[Douyin] 7424444791112338697
  实际时间 / Actual time: 2024-10-11 02:17:53 (ts: 1728638273)
  解码时间 / Decoded time: 2024-10-11T09:17:39+00:00 (ts: 1728638259)
  ✗ 误差 / Error (-14秒 / seconds)

[Douyin] 7395877358483328307
  实际时间 / Actual time: 2024-07-26 02:41:34 (ts: 1721986894)
  解码时间 / Decoded time: 2024-07-26T09:41:25+00:00 (ts: 1721986885)
  ✗ 误差 / Error (-9秒 / seconds)

[Douyin] 7391056989352168714
  实际时间 / Actual time: 2024-07-13 02:56:12 (ts: 1720864572)
  解码时间 / Decoded time: 2024-07-13T09:55:56+00:00 (ts: 1720864556)
  ✗ 误差 / Error (-16秒 / seconds)

[TikTok] 7559684939684400414
  实际时间 / Actual time: 2025-10-10 12:58:53 (ts: 1760126333)
  解码时间 / Decoded time: 2025-10-10T19:58:30+00:00 (ts: 1760126310)
  ✗ 误差 / Error (-23秒 / seconds)

[TikTok] 7559661864628538654
  实际时间 / Actual time: 2025-10-10 11:29:21 (ts: 1760120961)
  解码时间 / Decoded time: 2025-10-10T18:28:57+00:00 (ts: 1760120937)
  ✗ 误差 / Error (-24秒 / seconds)

[TikTok] 7559607368695188766
  实际时间 / Actual time: 2025-10-10 07:57:50 (ts: 1760108270)
  解码时间 / Decoded time: 2025-10-10T14:57:29+00:00 (ts: 1760108249)
  ✗ 误差 / Error (-21秒 / seconds)

[TikTok] 7559309513321352478
  实际时间 / Actual time: 2025-10-09 12:41:46 (ts: 1760038906)
  解码时间 / Decoded time: 2025-10-09T19:41:39+00:00 (ts: 1760038899)
  ✗ 误差 / Error (-7秒 / seconds)

[TikTok] 7558891371189325087
  实际时间 / Actual time: 2025-10-08 09:39:16 (ts: 1759941556)
  解码时间 / Decoded time: 2025-10-08T16:39:03+00:00 (ts: 1759941543)
  ✗ 误差 / Error (-13秒 / seconds)

[TikTok] 7558545854957751582
  实际时间 / Actual time: 2025-10-07 11:18:30 (ts: 1759861110)
  解码时间 / Decoded time: 2025-10-07T18:18:16+00:00 (ts: 1759861096)
  ✗ 误差 / Error (-14秒 / seconds)

[TikTok] 7558516087483010334
  实际时间 / Actual time: 2025-10-07 09:22:54 (ts: 1759854174)
  解码时间 / Decoded time: 2025-10-07T16:22:45+00:00 (ts: 1759854165)
  ✗ 误差 / Error (-9秒 / seconds)

[TikTok] 7558500140869225758
  实际时间 / Actual time: 2025-10-07 08:21:08 (ts: 1759850468)
  解码时间 / Decoded time: 2025-10-07T15:20:52+00:00 (ts: 1759850452)
  ✗ 误差 / Error (-16秒 / seconds)

[TikTok] 7558142881173671199
  实际时间 / Actual time: 2025-10-06 09:15:02 (ts: 1759767302)
  解码时间 / Decoded time: 2025-10-06T16:14:31+00:00 (ts: 1759767271)
  ✗ 误差 / Error (-31秒 / seconds)

[TikTok] 7557835825778560287
  实际时间 / Actual time: 2025-10-05 13:23:19 (ts: 1759695799)
  解码时间 / Decoded time: 2025-10-05T20:22:59+00:00 (ts: 1759695779)
  ✗ 误差 / Error (-20秒 / seconds)

[TikTok] 7556652844523310367
  实际时间 / Actual time: 2025-10-02 08:52:53 (ts: 1759420373)
  解码时间 / Decoded time: 2025-10-02T15:52:25+00:00 (ts: 1759420345)
  ✗ 误差 / Error (-28秒 / seconds)

[TikTok] 7556308975713537311
  实际时间 / Actual time: 2025-10-01 10:38:28 (ts: 1759340308)
  解码时间 / Decoded time: 2025-10-01T17:38:02+00:00 (ts: 1759340282)
  ✗ 误差 / Error (-26秒 / seconds)

[TikTok] 7556036906631318815
  实际时间 / Actual time: 2025-09-30 17:02:28 (ts: 1759276948)
  解码时间 / Decoded time: 2025-10-01T00:02:16+00:00 (ts: 1759276936)
  ✗ 误差 / Error (-12秒 / seconds)

[TikTok] 7556029213594144031
  实际时间 / Actual time: 2025-09-30 16:32:40 (ts: 1759275160)
  解码时间 / Decoded time: 2025-09-30T23:32:25+00:00 (ts: 1759275145)
  ✗ 误差 / Error (-15秒 / seconds)

[TikTok] 7556009541616389407
  实际时间 / Actual time: 2025-09-30 15:16:29 (ts: 1759270589)
  解码时间 / Decoded time: 2025-09-30T22:16:05+00:00 (ts: 1759270565)
  ✗ 误差 / Error (-24秒 / seconds)

[TikTok] 7555940417905134878
  实际时间 / Actual time: 2025-09-30 10:48:20 (ts: 1759254500)
  解码时间 / Decoded time: 2025-09-30T17:47:50+00:00 (ts: 1759254470)
  ✗ 误差 / Error (-30秒 / seconds)

[TikTok] 7555600450024721694
  实际时间 / Actual time: 2025-09-29 12:48:47 (ts: 1759175327)
  解码时间 / Decoded time: 2025-09-29T19:48:35+00:00 (ts: 1759175315)
  ✗ 误差 / Error (-12秒 / seconds)

[TikTok] 7555526907157040414
  实际时间 / Actual time: 2025-09-29 08:03:22 (ts: 1759158202)
  解码时间 / Decoded time: 2025-09-29T15:03:12+00:00 (ts: 1759158192)
  ✗ 误差 / Error (-10秒 / seconds)

[TikTok] 7554123030205713694
  实际时间 / Actual time: 2025-09-25 13:15:39 (ts: 1758831339)
  解码时间 / Decoded time: 2025-09-25T20:15:27+00:00 (ts: 1758831327)
  ✗ 误差 / Error (-12秒 / seconds)

[TikTok] 7554044414469917982
  实际时间 / Actual time: 2025-09-25 08:10:32 (ts: 1758813032)
  解码时间 / Decoded time: 2025-09-25T15:10:23+00:00 (ts: 1758813023)
  ✗ 误差 / Error (-9秒 / seconds)

================================================================================
=== 验证结果统计 / Validation Results Statistics ===
================================================================================

📊 总体准确性 / Overall Accuracy:
  验证总数 / Total validated:     73
  完全匹配 / Exact match:     0 (0.0%)
  接近匹配 / Close match:     19 (26.0%)
  较大误差 / Large error:     54

📈 误差分析 (绝对值) / Error Analysis (Absolute Value):
  平均误差 / Average error:     14.30 秒 / seconds
  最大误差 / Maximum error:     49 秒 / seconds
  最小误差 / Minimum error:     2 秒 / seconds

🇨🇳 抖音平台 / Douyin Platform:
  数量 / Count:         53
  平均误差 / Average error:     -12.98 秒 / seconds
  误差范围 / Error range:     -49 ~ -2 秒 / seconds

🌐 TikTok平台 / TikTok Platform:
  数量 / Count:         20
  平均误差 / Average error:     -17.80 秒 / seconds
  误差范围 / Error range:     -31 ~ -7 秒 / seconds

8) Project Structure

./
├── decode_aweme_id.py          # 🔧 Core script (decode/analyze/validate)
├── test_validation.py          # ✅ Quick validation script
├── aweme_ids_output.json       # 📊 Real sample data (73 entries)
├── index.html                  # 🌐 Online decoder (GitHub Pages)
├── screenshots/                # 📸 Interface screenshots
│   ├── Index.png              #    Chinese interface preview
│   └── Index-EN.png           #    English interface preview
├── README.md                   # 📖 Chinese Documentation
├── README-EN.md                # 📖 English Documentation
└── config.example.toml         # ⚙️  Optional config (calibration params)

Optional Configuration File

config.example.toml:

[calibration]
# Second-level calibration parameters (based on error analysis)
douyin_offset_seconds = 13
tiktok_offset_seconds = 18

[analysis]
# Analysis parameters
close_match_threshold = 5  # Close match threshold (seconds)

9) Real-world Applications

🎯 Data Analysis

Time-series Research: Filter videos by time range without database queries
Publishing Pattern Analysis: Identify peak hours, publishing frequency
Content Freshness: Quickly determine content age

🔍 System Monitoring

Distributed Tracing: Locate server/IDC via shard ID
Performance Analysis: Estimate QPS, load balancing effectiveness via sequence numbers
Anomaly Detection: Identify batch uploads, crawler behavior

🛡️ Risk Control & Security

Batch Identification: Same sequence numbers may indicate batch operations
Time Verification: Validate ID-business time reasonableness
Deduplication Optimization: Efficient deduplication strategies based on ID structure

📊 Data Collection

Incremental Collection: Precisely control collection window by timestamp range
Data Backtracking: Quickly locate content from specific time periods
Collection Quality: Verify time-series completeness of collected data

10) FAQ

Q1: Why isn't exact match rate 100%?

A: This is normal. ID timestamp is the generation moment, while API-returned create_time might be review approval, content online, or other moments. Errors are typically within ±20 seconds.

Q2: How to improve match with create_time?

A: Use calibration scheme in §5, add platform-specific offsets:

Douyin: +13 seconds
TikTok: +18 seconds

Q3: Can low 32 bits be fully recovered?

A: Cannot be directly recovered from public information. Recommend using §6 methods, through same-second sampling and statistical analysis to find the most suitable division scheme for your data.

Q4: Which timezones are supported?

A: Defaults to UTC and Los Angeles timezone. You can modify the LA_TZ variable in the script to any timezone (e.g., Asia/Shanghai).

Q5: Can reverse-constructed IDs be used in production?

A: No. forge_aweme_like_id() is only for testing, sorting, integration testing, and other local scenarios; doesn't correspond to real platform content.

Q6: What data does validation require?

A: Requires JSON file containing aweme_id, create_time, source fields. Format reference: aweme_ids_output.json.

Q7: How to add my own test data?

A: Organize your data according to §7 JSON format, save as aweme_ids_output.json, then run validate_decode_algorithm().

11) Version Information

Info	Content
Version	v2.1 (2025)
Author	Evil0ctal (Adam)
License	MIT License
Python	3.9+
Dependencies	No external dependencies

12) Changelog

v2.1 (2025-10)

🌐 Added Online Decoder: Deployed on GitHub Pages, use without installation
🌍 Bilingual Support: Auto-detects browser language with manual toggle
📱 Responsive Design: Perfect support for mobile and desktop devices
🎨 Dark Mode Support: Automatically adapts to system theme preference
🔍 SEO Optimization: Added Open Graph, Twitter Card, Schema.org tags
📸 Interface Screenshots: Added Chinese and English interface previews

v2.0 (2025-01)

✨ Added validate_decode_algorithm() auto-validation feature
✨ Added test_validation.py quick test script
📊 Complete validation report based on 73 real data samples
📖 Restructured documentation, improved structure and readability
🌐 Added English documentation README-EN.md

v1.0 (2024)

🎉 Initial release
🔧 Basic decoding functionality
📊 Low 32-bit multi-scheme analysis
📈 Statistical analysis and visualization

13) Acknowledgments

💙 Thanks to TikHub.io for providing real sample data
🙏 Thanks to the open-source community for Snowflake ID algorithm research
🌟 Thanks to all contributors and users for feedback

14) Contact

GitHub: @evil0ctal
Project: Douyin-TikTok-Video-ID-Decoder
Issue Tracker: GitHub Issues

⭐ If this project helps you, please give it a Star!

Made with ❤️ by Evil0ctal

FilesExpand file tree

README-EN.md

Latest commit

History