dataforge-labs/ethereum-mempool

Ethereum mempool observations

Ethereum pending-transaction activity, lifecycle observations and comparisons between provider views. The continuing panels contain minute-level summaries, transactions classified as dropped and differences between observed pending sets.

Contents

Table	Record
ethereum_mempool_minute_summary	A minute of pending-transaction activity
ethereum_dropped_transactions	A transaction classified as dropped during observation
ethereum_transaction_lifecycle	A transaction lifecycle record; this series ends on 2026-08-26
ethereum_mempool_comparison	A comparison of pending sets from several providers

Using the data

Use the minute-level table for aggregate activity and the divergence table to assess differences between observed mempools. General per-transaction lifecycle collection ended on 2026-08-26; the minute-level and dropped-transaction panels continued.

For a separate source of Ethereum mempool observations, see the Flashbots Mempool Dumpster. Measurements from different observers may differ because of connectivity, transaction visibility and collection methods.

Limitations

• A mempool is local to a node. These observations do not cover all public or private transaction flow.
• A dropped classification requires absence from the observed pending views and a chain check after a debounce period. It describes the collector's observation window, not a guarantee that the transaction can never be mined or rebroadcast.
• Timestamps reflect the collector's observations and include polling and network delays.
• Minute-level summaries cannot recover individual transaction paths. Check the available dates before selecting a table for transaction-level work.

Files and access

Data is stored as Parquet files under table_name/YYYY/MM/, with partitions for collection windows. Each measurement table has a fixed 7-day sample beginning at its configured collection start date. The sample windows in this repository span 2026-08-25 to 2026-09-01. Availability within each window depends on successful collection. The public sample dates remain fixed as additional history accumulates privately. Contact DataForge through the discussions tab to enquire about additional history.

Load a table

Install datasets and pandas to run this example. The train split contains all observations in the selected table; it is not a predefined modelling split.

from datasets import load_dataset

data = load_dataset("dataforge-labs/ethereum-mempool",
                    "ethereum_mempool_minute_summary", split="train")
df = data.to_pandas()

Coverage

collection_runs records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.

License and contact

The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.

Earlier file paths

Each table is stored under a directory with the same descriptive name. The file contents and date partitions are unchanged. Scripts using an earlier directory name should use the corresponding table name below, or pin downloads to revision before-folder-rename-20260915 to access the original layout. Internal collector IDs are retained in the private archive and may appear in raw coverage records.

Current table and directory	Earlier directory
ethereum_mempool_minute_summary	e1_mempool_minutely/
ethereum_dropped_transactions	e1_mempool_dropped/
ethereum_transaction_lifecycle	e1_mempool_lifecycle/
ethereum_mempool_comparison	e3_mempool_divergence/
collection_runs	e0_run_manifest/