dataforge-labs/l2-preconfirmation-reliability

L2 sequencer preconfirmation observations

Periodic observations of unsafe and safe heads on selected OP Stack networks, with subsequent checks of sampled unsafe block hashes against the chain reported by the endpoint. The panel records coverage and detected changes to previously observed blocks.

Contents

Table	Record
l2_preconfirmation_checks	A heartbeat with head and check statistics, or a detected violation

Using the data

row_type separates heartbeats from violations. Use n_checked and n_failed to assess how many checks completed before interpreting a period with no detected violations.

lag_blocks measures the reported gap between unsafe and safe heads. Use it only where safe_tag_plausible is true. Some endpoints have returned stale or inconsistent safe tags, which can produce implausibly large lags.

Compare lag with each chain's own history. Different sequencer and batch-publication policies can produce different normal levels across networks.

Limitations

• The configured coverage includes nine OP Stack chains. Results do not establish behaviour across other rollup architectures.
• Sampling can miss a block that is proposed and replaced between observations.
• Observations depend on public endpoint responses. A mismatch warrants investigation and is not, by itself, proof of a sequencer failure or an L1 finality failure.
• An unsafe head is an early chain view, not a universal transaction-level confirmation guarantee.
• No detected violations means none were found in completed checks; it does not establish uninterrupted reliability outside those checks.

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 2026-08-26 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/l2-preconfirmation-reliability",
                    "l2_preconfirmation_checks", 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
l2_preconfirmation_checks	e14_l2_preconf/
collection_runs	e0_run_manifest/