Argo-Bench

One simulated year of a New York City food-delivery company, exported to an Oracle E-Business Suite 12.2 warehouse of 235 tables and 7.54 billion rows.

Leaderboard · How it works · Paper · Code

An enterprise ERP you can download

The data that enterprise analytics runs on is rarely public. Large companies keep their orders, payouts, ledgers and customer records in ERP systems such as Oracle E-Business Suite and SAP, extended with custom tables of their own, and access to them is tightly restricted. Benchmarks therefore run on public datasets, which tend to be collections of loosely related tables rather than one system whose modules have to agree, and their answer keys are only as good as the people who wrote them. On real data, fraud that was never caught has no label, and a decision that was never taken has no outcome.

Argo-Bench simulates the business instead. The warehouse can be released whole because no one in it is real, and every answer key comes from the simulator's own state rather than from an annotator.

What's in the warehouse

• A calibrated company. 81 million orders from 17,902 real New York restaurants, delivered by 81,116 couriers over calendar 2024. The world is built from 34 public datasets and reports, and calibrated to the delivery-app economics New York City publishes each quarter and to the platforms' public filings. It absorbs a real shock: the city raised the minimum pay for delivery workers to $19.56 an hour on April 1, 2024.
• One system. 159 standard EBS tables across GL, AR, AP, OM, XLA, HZ, OKC/OKS, ZX and more, where every standard table and column exists in Oracle's EBS 12.2 data dictionary, and 76 custom XX_ tables for dispatch, courier pay, incentives, promotions, sessions and support. An order resolves into dispatch decisions, courier pay, merchant payouts and balanced general-ledger journals, and the modules reconcile. The schema was designed with three ERP consultants.
• Fraud and abuse where public evidence says it happens. Couriers who steal orders or spoof their GPS, promotion-farming rings, account takeovers, shell storefronts and diverted payouts. Each pattern is calibrated to how often honest customers share a device, an address or a card, which is what makes them hard to tell apart.
• As-of views. Eleven month schemas cut every table to what existed at the end of each month, so a task can be posed as of any month-end and graded on the months after it.

Running the benchmark

The 210 tasks cover trust and safety, marketplace operations, FP&A, accounting and growth. Agents query the warehouse, analyze it in a sandboxed Python environment, and file bans, forecasts, budgets, reported figures and dashboard data sources. The grader scores each filing against the simulation's hidden state. In the paper, the strongest of 12 models, Claude Opus 5.5, averages 59.5 points and scores 95 or more on 34.8% of tasks.

The tasks, the reference agent and its sandboxes are at github.com/TextQLLabs/Argo-Bench, which also explains how to export a run for official scoring.

Argo-Bench is built by TextQL.

Layout

data/<TABLE>/*.parquet        the rows (monthly parts 2024-MM.parquet, or part-0.parquet)
data/<TABLE>/_delta_log/      a Delta Lake log over the same files (relative paths)
tables.json                   every table: rows, files, columns with types, partition column
setup/                        loaders and month views per engine (below)

Every column has one of six types, the same in every engine:

tables.json type	Parquet	BigQuery	Snowflake	Spark / Delta	Trino	Iceberg
INT64	int64	INT64	NUMBER(19,0)	BIGINT	bigint	long
FLOAT64	double	FLOAT64	FLOAT	DOUBLE	double	double
STRING	string	STRING	VARCHAR	STRING	varchar	string
DATETIME	timestamp[us], no zone	DATETIME	TIMESTAMP_NTZ(6)	TIMESTAMP_NTZ	timestamp(6)	timestamp
DATE	date32	DATE	DATE	DATE	date	date
BOOLEAN	bool	BOOL	BOOLEAN	BOOLEAN	boolean	boolean

Timestamps are wall-clock times with no zone. Table and column names are upper case. Keep them that way: queries and the month views quote them.

Get the files

pip install -U "huggingface_hub[hf_xet]"
hf download textql/Argo-Bench --repo-type dataset --local-dir argo-bench
# or a few tables:
hf download textql/Argo-Bench --repo-type dataset --local-dir argo-bench \
    --include "data/GL_PERIODS/*" --include "tables.json" --include "setup/*"

The SQL files under setup/ refer to the data/ directory of your copy as __DATA_ROOT__. Fill it in once for the engine you use, for example:

sed -i.bak 's#__DATA_ROOT__#s3://my-bucket/argo-bench/data#g' setup/spark/register_delta.sql

Every engine below reads the same Parquet files. Loading into BigQuery or Snowflake copies them into that warehouse. Delta Lake, Iceberg, Trino and DuckDB read them in place.

DuckDB (no download needed)

sed 's#__DATA_ROOT__#hf://datasets/textql/Argo-Bench/data#g' setup/duckdb/views.sql > views.sql
duckdb argo.duckdb -c ".read views.sql" -c ".read setup/duckdb/month_views.sql"
duckdb argo.duckdb -c 'SELECT COUNT(*) FROM food_delivery."XX_DISPUTES"'

Over hf:// the base views take about 2 minutes and the month views about 7 (DuckDB reads Parquet footers over HTTP as it binds each view). Only the rows a query touches are fetched. Point __DATA_ROOT__ at a local data/ directory to make both near-instant. DuckDB can also read the Delta tables: SELECT * FROM delta_scan('argo-bench/data/GL_PERIODS').

BigQuery

BigQuery loads from Google Cloud Storage, so copy data/ into a bucket first. Use a bucket in (or inside) your dataset's location. Load jobs are free. The loaded dataset holds about 880 GiB of logical storage.

gcloud storage cp -r argo-bench/data gs://my-bucket/argo-bench/
RELEASE_URI=gs://my-bucket/argo-bench BQ_PROJECT=my-project BQ_DATASET=food_delivery \
    bash setup/bigquery/load.sh
BQ_PROJECT=my-project BQ_DATASET=food_delivery bash setup/bigquery/month_views.sh

load.sh creates each table with its final types and loads it with one Parquet load job. 94 tables are MONTH-partitioned on the column the month views cut on, so a month view scans only its months. JOBS=N loads N tables at a time (default 8), ONLY=A,B just those tables, and BQ_LOCATION defaults to US. With AUTHORIZE=1, month_views.sh also makes each month dataset an authorized dataset on the base, so a reader granted only food_delivery_6 cannot read past June (needs jq).

Snowflake

Create a stage named "food_delivery"."ARGO_BENCH" whose root holds data/, then run load.sql and month_views.sql in a database of your choice.

• Your copy in S3, GCS or Azure (external stage):

  USE DATABASE my_db;
  CREATE SCHEMA IF NOT EXISTS "food_delivery";
  CREATE STAGE "food_delivery"."ARGO_BENCH"
    URL = 's3://my-bucket/argo-bench/' STORAGE_INTEGRATION = my_integration;
  

• A local download (internal stage): fill in __LOCAL_ROOT__ in setup/snowflake/put.sql, then snowsql -d my_db -f setup/snowflake/put.sql.

snowsql -d my_db -f setup/snowflake/load.sql
snowsql -d my_db -f setup/snowflake/month_views.sql

(snow sql -f ... with the Snowflake CLI works too.) load.sql creates each table with its final types, then runs one COPY INTO ... MATCH_BY_COLUMN_NAME = CASE_SENSITIVE per table. The schema name is the quoted, lower-case "food_delivery".

Delta Lake (Databricks, Spark, delta-rs)

Each data/<TABLE>/ is already a Delta table. Its log names the files by relative path, so it works wherever the directory is copied: local disk, S3, GCS, ADLS, or a Databricks volume. Register the tables and add the month views:

sed -i.bak 's#__DATA_ROOT__#s3://my-bucket/argo-bench/data#g' setup/spark/register_delta.sql
# Databricks: run both files in a SQL editor or notebook, in the catalog you want.
spark-sql --packages io.delta:delta-spark_2.13:4.0.0 \
  --conf spark.sql.extensions=io.delta.sql.DeltaSparkSessionExtension \
  --conf spark.sql.catalog.spark_catalog=org.apache.spark.sql.delta.catalog.DeltaCatalog \
  -f setup/spark/register_delta.sql      # then the same with -f setup/spark/month_views.sql

Delta tables with timestamps use the timestampNtz table feature (reader version 3), which Databricks Runtime 13.3+, Delta 3+, delta-rs and DuckDB support. From Python, without Spark: deltalake.DeltaTable("argo-bench/data/XX_DISPUTES").to_pyarrow_table().

Iceberg

Iceberg metadata names files by absolute path, so it is written for your copy where it lives. setup/iceberg/register.py creates each table from its Parquet schema and adds the existing files (pyiceberg add_files: manifests, statistics and a name mapping are written, and no data is copied):

pip install "pyiceberg[pyarrow,sql-sqlite]"
# local copy, local SQLite catalog in ./iceberg/
python setup/iceberg/register.py --root argo-bench
# a copy in object storage, into your catalog (REST, Glue, Hive, SQL, ...)
python setup/iceberg/register.py --root s3://my-bucket/argo-bench \
    --catalog prod -P type=rest -P uri=https://catalog.example.com -P warehouse=wh

Then query food_delivery.<TABLE> from any engine attached to that catalog (Spark, Trino, Snowflake, DuckDB, PyIceberg). setup/spark/month_views.sql and setup/trino/month_views.sql create the month views in that catalog as well.

Trino

Trino reads the Delta tables in place through its Delta Lake connector:

1. Copy setup/trino/delta.properties to etc/catalog/delta.properties. Set local.location to your download, or switch to the S3/GCS lines. Restart Trino.
2. Register the tables, then add the month views:

sed -i.bak 's#__DATA_ROOT__#local:///data#g' setup/trino/register_delta.sql
trino --catalog delta -f setup/trino/register_delta.sql
trino --catalog delta -f setup/trino/month_views.sql
trino --catalog delta --schema food_delivery --execute 'SELECT COUNT(*) FROM xx_disputes'

For an Iceberg catalog instead, register with setup/iceberg/register.py into a catalog Trino's Iceberg connector also uses (REST, Glue, Hive metastore, JDBC). Then run month_views.sql with --catalog set to it.

Month views (as-of datasets)

Tasks are posed as of the end of a month. The full-year tables are the base (food_delivery). Eleven sibling schemas hold views that cut every table to what existed at the end of each month:

schema	rows kept
food_delivery_1	before 2024-02-01
food_delivery_2	before 2024-03-01
food_delivery_3	before 2024-04-01
food_delivery_4	before 2024-05-01
food_delivery_5	before 2024-06-01
food_delivery_6	before 2024-07-01
food_delivery_7	before 2024-08-01
food_delivery_8	before 2024-09-01
food_delivery_9	before 2024-10-01
food_delivery_10	before 2024-11-01
food_delivery_11	before 2024-12-01
food_delivery	the whole year

A view keeps a row when its creation instant (for most tables CREATION_DATE, for some a table-specific column) falls before the cutoff. Reference tables pass through whole, a few tables are period-keyed, and a few are left out of the month schemas. Each engine's month_views file carries every table's exact rule. In BigQuery (with AUTHORIZE=1), Snowflake, Databricks Unity Catalog and Trino a view reads the base with its owner's privileges, so a role granted only one month schema can read that month and not the full year.

Tables

prefix	tables	rows
XX_	76	2,632,600,570
XLA_	7	1,323,813,636
GL_	14	906,523,987
RA_	9	869,947,258
AR_	13	766,451,904
OE_	6	581,051,178
ZX_	8	158,424,118
HZ_	12	97,461,203
CE_	10	95,989,462
AP_	12	48,567,646
OKS_	6	25,621,703
OKC_	9	17,185,555
IBY_	6	11,182,364
MTL_	9	3,130,951
QP_	5	136,026
HR_	4	53,710
FND_	29	17,416

All 235 tables

table	rows	columns	files	size
AP_CHECKS_ALL	5,239,635	22	1	75.1 MiB
AP_HOLDS_ALL	31,634	14	1	528.8 KiB
AP_HOLD_CODES	13	11	1	3.8 KiB
AP_INVOICES_ALL	6,051,283	36	1	106.9 MiB
AP_INVOICE_DISTRIBUTIONS_ALL	7,881,526	19	1	33.0 MiB
AP_INVOICE_LINES_ALL	17,470,894	17	1	120.5 MiB
AP_INVOICE_PAYMENTS_ALL	5,643,336	15	1	69.1 MiB
AP_PAYMENT_SCHEDULES_ALL	6,051,283	13	1	54.6 MiB
AP_SUPPLIERS	99,018	33	1	2.0 MiB
AP_SUPPLIER_SITES_ALL	99,018	19	1	1.1 MiB
AP_TERMS_LINES	3	9	1	3.1 KiB
AP_TERMS_TL	3	11	1	3.5 KiB
AR_ADJUSTMENTS_ALL	0	25	12	31.9 KiB
AR_AGING_BUCKETS	1	9	1	2.9 KiB
AR_AGING_BUCKET_LINES_B	5	11	1	3.8 KiB
AR_AGING_BUCKET_LINES_TL	5	9	1	3.0 KiB
AR_BATCHES_ALL	478	22	12	105.4 KiB
AR_CASH_RECEIPTS_ALL	85,702,095	39	12	741.6 MiB
AR_CASH_RECEIPT_HISTORY_ALL	257,303,576	25	12	1.8 GiB
AR_DISTRIBUTIONS_ALL	158,503,648	12	12	828.8 MiB
AR_PAYMENT_SCHEDULES_ALL	174,181,429	29	24	2.3 GiB
AR_RECEIPT_CLASSES	1	12	1	3.8 KiB
AR_RECEIPT_METHODS	2	9	1	3.2 KiB
AR_RECEIVABLES_TRX_ALL	2	12	1	4.0 KiB
AR_RECEIVABLE_APPLICATIONS_ALL	90,760,662	27	12	1.1 GiB
CE_BANK_ACCOUNTS	1	34	1	10.6 KiB
CE_BANK_ACCT_USES_ALL	1	21	1	6.2 KiB
CE_GL_ACCOUNTS_CCID	1	13	1	4.2 KiB
CE_STATEMENT_HEADERS	251	20	1	19.1 KiB
CE_STATEMENT_HEADERS_INT	0	13	1	1.6 KiB
CE_STATEMENT_LINES	5,395,730	19	12	41.3 MiB
CE_STATEMENT_LINES_INTERFACE	0	14	1	1.6 KiB
CE_STATEMENT_RECONCILS_ALL	90,593,471	18	12	356.9 MiB
CE_SYSTEM_PARAMETERS	1	16	1	5.1 KiB
CE_TRANSACTION_CODES	6	16	1	5.1 KiB
FND_APPLICATION	12	9	1	3.3 KiB
FND_APPLICATION_TL	12	9	1	3.2 KiB
FND_CONCURRENT_PROGRAMS	7	10	1	3.5 KiB
FND_CONCURRENT_PROGRAMS_TL	7	11	1	4.1 KiB
FND_CONCURRENT_REQUESTS	15,764	13	1	265.3 KiB
FND_CURRENCIES	68	11	1	4.4 KiB
FND_CURRENCIES_TL	68	9	1	3.8 KiB
FND_FLEX_HIERARCHIES	2	8	1	2.8 KiB
FND_FLEX_HIERARCHIES_TL	2	11	1	4.1 KiB
FND_FLEX_VALIDATION_QUALIFIERS	5	6	1	2.3 KiB
FND_FLEX_VALUES	351	18	1	10.9 KiB
FND_FLEX_VALUES_TL	351	10	1	10.3 KiB
FND_FLEX_VALUE_HIERARCHIES	33	11	1	3.7 KiB
FND_FLEX_VALUE_NORM_HIERARCHY	26	12	1	4.5 KiB
FND_FLEX_VALUE_SETS	7	11	1	3.9 KiB
FND_ID_FLEXS	1	14	1	4.7 KiB
FND_ID_FLEX_SEGMENTS	7	17	1	5.5 KiB
FND_ID_FLEX_SEGMENTS_TL	7	13	1	4.3 KiB
FND_ID_FLEX_STRUCTURES	1	14	1	4.6 KiB
FND_ID_FLEX_STRUCTURES_TL	1	12	1	4.1 KiB
FND_LANGUAGES	29	10	1	3.9 KiB
FND_LOOKUP_TYPES	74	10	1	5.2 KiB
FND_LOOKUP_TYPES_TL	74	12	1	7.1 KiB
FND_LOOKUP_VALUES	292	13	1	8.4 KiB
FND_RESPONSIBILITY	8	12	1	4.3 KiB
FND_RESPONSIBILITY_TL	8	10	1	3.5 KiB
FND_TERRITORIES	78	7	1	2.6 KiB
FND_TERRITORIES_TL	78	9	1	3.8 KiB
FND_USER	43	10	1	4.7 KiB
GL_ACCOUNT_HIERARCHIES	49,294	7	1	346.3 KiB
GL_BALANCES	204,547	22	1	3.2 MiB
GL_CODE_COMBINATIONS	10,666	19	1	74.6 KiB
GL_IMPORT_REFERENCES	904,430,409	17	36	5.9 GiB
GL_INTERFACE	0	17	1	1.8 KiB
GL_JE_BATCHES	3,047	15	36	213.3 KiB
GL_JE_CATEGORIES_TL	9	10	1	3.4 KiB
GL_JE_HEADERS	4,354	19	36	274.3 KiB
GL_JE_LINES	1,821,518	17	36	17.1 MiB
GL_JE_SOURCES_TL	9	15	1	4.9 KiB
GL_LEDGERS	1	45	1	13.8 KiB
GL_PERIODS	26	17	1	6.3 KiB
GL_PERIOD_STATUSES	104	20	1	9.2 KiB
GL_SUMMARY_TEMPLATES	3	21	1	7.0 KiB
HR_ALL_ORGANIZATION_UNITS	17,905	16	1	350.5 KiB
HR_LOCATIONS_ALL	17,902	17	1	464.6 KiB
HR_OPERATING_UNITS	1	6	1	2.0 KiB
HR_ORGANIZATION_INFORMATION	17,902	14	1	317.4 KiB
HZ_CODE_ASSIGNMENTS	28	18	1	6.1 KiB
HZ_CONTACT_POINTS	8,452,790	18	1	152.8 MiB
HZ_CUSTOMER_PROFILES	4,208,326	22	1	37.0 MiB
HZ_CUST_ACCOUNTS	4,208,326	14	1	40.4 MiB
HZ_CUST_ACCOUNT_ROLES	0	15	1	1.7 KiB
HZ_CUST_ACCT_SITES_ALL	18,487,230	17	1	273.8 MiB
HZ_CUST_PROFILE_CLASSES	1	21	1	6.1 KiB
HZ_CUST_SITE_USES_ALL	22,695,556	17	1	323.1 MiB
HZ_LOCATIONS	16,578,510	34	1	351.1 MiB
HZ_ORGANIZATION_PROFILES	17,931	17	1	398.1 KiB
HZ_PARTIES	4,307,373	31	1	121.2 MiB
HZ_PARTY_SITES	18,505,132	15	1	265.0 MiB
IBY_DOCS_PAYABLE_ALL	5,643,336	31	1	128.0 MiB
IBY_EXTERNAL_PAYEES_ALL	99,018	12	1	1.2 MiB
IBY_EXT_BANK_ACCOUNTS	98,404	18	1	1.3 MiB
IBY_PAYMENTS_ALL	5,239,635	34	1	86.7 MiB
IBY_PAY_INSTRUCTIONS_ALL	1,071	23	1	28.2 KiB
IBY_PMT_INSTR_USES_ALL	100,900	14	1	1.3 MiB
MTL_CATEGORIES_B	70	10	1	4.0 KiB
MTL_CATEGORIES_TL	70	9	1	3.7 KiB
MTL_CATEGORY_SETS_B	1	11	1	3.7 KiB
MTL_CATEGORY_SETS_TL	1	9	1	3.0 KiB
MTL_ITEM_CATEGORIES	1,037,635	13	1	1.2 MiB
MTL_PARAMETERS	17,903	12	1	157.7 KiB
MTL_SYSTEM_ITEMS_B	1,037,635	32	1	4.5 MiB
MTL_SYSTEM_ITEMS_TL	1,037,635	10	1	4.1 MiB
MTL_UNITS_OF_MEASURE_TL	1	17	1	4.8 KiB
OE_ORDER_HEADERS_ALL	80,908,384	41	12	2.9 GiB
OE_ORDER_LINES_ALL	446,720,613	24	12	4.4 GiB
OE_ORDER_SOURCES	40	12	1	5.7 KiB
OE_PRICE_ADJUSTMENTS	53,422,133	35	12	614.3 MiB
OE_TRANSACTION_TYPES_ALL	4	13	1	4.2 KiB
OE_TRANSACTION_TYPES_TL	4	13	1	4.3 KiB
OKC_K_HEADERS_ALL_B	1,611,303	53	1	31.9 MiB
OKC_K_HEADERS_TL	1,611,303	11	1	16.5 MiB
OKC_K_ITEMS	2,505,907	21	1	40.7 MiB
OKC_K_LINES_B	2,505,907	49	1	64.5 MiB
OKC_K_LINES_TL	2,505,907	11	1	26.5 MiB
OKC_K_PARTY_ROLES_B	3,222,606	17	1	72.6 MiB
OKC_K_PARTY_ROLES_TL	3,222,606	9	1	31.0 MiB
OKC_STATUSES_B	8	11	1	3.6 KiB
OKC_STATUSES_TL	8	11	1	3.4 KiB
OKS_BILL_CONT_LINES	5,907,180	33	12	31.5 MiB
OKS_BILL_TXN_LINES	5,907,180	18	12	27.1 MiB
OKS_K_LINES_B	2,505,907	29	1	47.7 MiB
OKS_K_LINES_TL	2,505,907	10	1	26.5 MiB
OKS_LEVEL_ELEMENTS	6,289,622	19	1	65.1 MiB
OKS_STREAM_LEVELS_B	2,505,907	24	1	45.1 MiB
QP_LIST_HEADERS_B	33,855	29	1	510.8 KiB
QP_LIST_HEADERS_TL	33,855	9	1	484.3 KiB
QP_LIST_LINES	34,103	33	1	512.3 KiB
QP_PRICING_ATTRIBUTES	34,211	23	1	458.0 KiB
QP_QUALIFIERS	2	24	1	7.2 KiB
RA_BATCH_SOURCES_ALL	3	10	1	3.6 KiB
RA_CUSTOMER_TRX_ALL	88,806,376	26	12	1.2 GiB
RA_CUSTOMER_TRX_LINES_ALL	535,185,608	20	12	5.0 GiB
RA_CUST_TRX_LINE_GL_DIST_ALL	245,955,258	20	12	1.8 GiB
RA_CUST_TRX_TYPES_ALL	4	20	1	6.1 KiB
RA_INTERFACE_LINES_ALL	0	18	1	2.1 KiB
RA_TERMS_B	3	12	1	3.9 KiB
RA_TERMS_LINES	3	9	1	3.2 KiB
RA_TERMS_TL	3	10	1	3.3 KiB
XLA_AE_HEADERS	166,682,629	22	13	651.2 MiB
XLA_AE_LINES	412,340,432	25	13	4.8 GiB
XLA_DISTRIBUTION_LINKS	412,340,432	7	13	1.9 GiB
XLA_EVENTS	166,682,629	18	13	474.9 MiB
XLA_EVENT_TYPES_B	5	12	1	3.9 KiB
XLA_EVENT_TYPES_TL	5	13	1	4.4 KiB
XLA_TRANSACTION_ENTITIES	165,767,504	12	13	395.8 MiB
XX_AUDIT_TRAIL	282,177	21	1	6.7 MiB
XX_CAMPAIGN_AUTHORITY_EVENTS	85,454	15	1	1.5 MiB
XX_CAMPAIGN_BUDGET_CREDITS	34,277	20	12	1.1 MiB
XX_CARD_FINGERPRINTS	7,377,322	12	1	206.1 MiB
XX_CARD_VERIFICATIONS	2,671,548	21	1	61.7 MiB
XX_COURIER_ACCOUNT_EVENTS	9,802	8	12	158.5 KiB
XX_COURIER_APPLICATIONS	40,167	8	12	695.3 KiB
XX_COURIER_APP_SESSIONS	6,075,323	9	12	75.8 MiB
XX_COURIER_DEVICES	115,306	6	12	951.5 KiB
XX_COURIER_EARNINGS	69,510,237	15	12	1.1 GiB
XX_COURIER_IDENTITY_CHECKS	154,540	11	12	2.6 MiB
XX_COURIER_VIOLATIONS	112,326	9	12	1.8 MiB
XX_COURIER_WAITS	21,842,232	22	12	713.9 MiB
XX_CUSTOMER_ACCOUNT_EVENTS	818,350	20	12	22.1 MiB
XX_CUSTOMER_DEVICES	9,778,766	13	1	148.5 MiB
XX_CUSTOMER_SESSIONS	283,206,289	16	12	4.3 GiB
XX_DELIVERY_ASSIGNMENTS	72,820,396	11	12	1.2 GiB
XX_DELIVERY_ATTEMPTS	1,971,172	17	12	31.5 MiB
XX_DELIVERY_LEGS	69,510,237	26	12	3.4 GiB
XX_DEVICES	9,394,404	13	1	260.6 MiB
XX_DISPATCH_ASSIGNMENTS	124,712,184	18	12	1.9 GiB
XX_DISPUTES	58,537	17	12	1.3 MiB
XX_DRIVER_LOCATION_OBSERVATIONS	134,480,113	8	12	1.3 GiB
XX_DRIVER_PROFILES	81,116	18	1	1.1 MiB
XX_DRIVER_QUESTS	145,600	16	1	1.8 MiB
XX_DRIVER_SHIFTS	6,102,273	15	12	118.2 MiB
XX_ENFORCEMENT_ACTIONS	11,065	17	1	70.8 KiB
XX_EXPERIMENT_ASSIGNMENTS	8,062	24	12	327.2 KiB
XX_FRAUD_WARNINGS	24,037	16	12	763.9 KiB
XX_GL_INTERFACE_HIST	492,089,977	9	36	1.6 GiB
XX_HOTSPOT_SNAPSHOTS	109,009	7	12	403.9 KiB
XX_INCENTIVE_COMMITMENTS	51,985,486	26	12	1.5 GiB
XX_INGESTION_RUNS	16,720	19	1	417.1 KiB
XX_INTEGRATION_INCIDENTS	23,873	15	12	578.3 KiB
XX_INTERFACE_ERRORS	2,237	20	1	60.4 KiB
XX_IP_ADDRESSES	2,665,687	17	1	18.5 MiB
XX_LOGIN_ATTEMPTS	33,214,593	15	12	660.2 MiB
XX_MARKETPLACE_ESTIMATES	2,605,900	22	12	38.1 MiB
XX_MEMBERSHIP_EVENTS	21,178,841	19	12	358.1 MiB
XX_MEMBERSHIP_ORDER_BENEFITS	44,815,297	19	12	698.4 MiB
XX_MERCHANT_CLOSURES	367,997	17	1	4.8 MiB
XX_MERCHANT_HANDOFF_CONFIRMATIONS	27,909,875	6	12	293.6 MiB
XX_MERCHANT_HOLIDAY_HOURS	87,710	14	1	662.9 KiB
XX_MERCHANT_HOURS	125,314	15	1	869.5 KiB
XX_MERCHANT_INTEGRATIONS	17,902	23	1	334.8 KiB
XX_MERCHANT_OPS_ACTIONS	3,444	26	12	193.6 KiB
XX_MERCHANT_RATING_SNAPSHOTS	5,072,584	14	12	18.2 MiB
XX_MERCHANT_READY_REPORTS	79,167,242	20	12	1.0 GiB
XX_MERCHANT_REGULATORY_EVENTS	12,273	15	1	199.0 KiB
XX_ORDER_PREP_QUOTES	80,908,384	16	12	1.0 GiB
XX_ORDER_PROMISES	71,535,421	3	12	418.7 MiB
XX_ORDER_REJECTIONS	427,040	18	12	8.0 MiB
XX_ORDER_STATUS_HIST	482,054,407	13	12	2.1 GiB
XX_PAYMENT_AUTHS	86,015,831	16	12	1.5 GiB
XX_PAYMENT_INSTRUMENTS	7,357,570	16	1	129.5 MiB
XX_PAYOUT_PERIODS	499	16	1	18.2 KiB
XX_PAY_PERIOD_INCENTIVES	53	32	1	16.8 KiB
XX_PREP_ESTIMATE_LOG	36,588,947	18	12	964.4 MiB
XX_PROMOTION_ALLOCATIONS	34,097,654	18	12	416.4 MiB
XX_PROMOTION_CHECKOUTS	99,173,355	11	12	1.2 GiB
XX_PROMOTION_CONTACTS	30,685,899	12	12	288.9 MiB
XX_PROMOTION_DECISIONS	13,530	22	12	552.9 KiB
XX_PROMOTION_SUBMISSIONS	42,666,911	8	12	439.7 MiB
XX_PROMOTION_VARIANTS	223	14	1	9.6 KiB
XX_PROMO_ATTEMPTS	35,040,794	20	12	704.4 MiB
XX_PROMO_CODES	34,106	20	1	786.1 KiB
XX_QUEST_INVITATIONS	15,762,145	14	1	243.6 MiB
XX_QUEST_PROGRESS	5,211,466	17	1	43.6 MiB
XX_QUEST_WINDOWS	145,600	14	1	2.4 MiB
XX_REFERRALS	508,522	15	1	13.1 MiB
XX_REFUNDS	1,875,438	20	12	59.2 MiB
XX_REVIEWS	9,521,121	16	12	166.3 MiB
XX_SUPPLY_FORECASTS	568,764	22	1	17.0 MiB
XX_SUPPLY_SNAPSHOTS	1,972,007	8	12	8.5 MiB
XX_SUPPORT_CASES	7,365,600	30	12	195.4 MiB
XX_TIP_ADJUSTMENTS	164,010	14	12	3.0 MiB
ZX_LINES	79,212,056	21	12	406.7 MiB
ZX_LINES_DET_FACTORS	79,212,056	22	12	384.6 MiB
ZX_RATES_B	1	18	1	5.5 KiB
ZX_RATES_TL	1	9	1	3.0 KiB
ZX_REGIMES_B	1	13	1	4.1 KiB
ZX_REGIMES_TL	1	9	1	3.0 KiB
ZX_TAXES_B	1	15	1	4.6 KiB
ZX_TAXES_TL	1	9	1	3.0 KiB

License

CC BY 4.0.