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Work with Deephaven for real-time data processing. Use for table queries, joins, aggregations, time-series, UI dashboards, plotting (dx), Kafka streaming, Iceberg integration. Triggers on mentions of Deephaven, tables or queries, and related concepts.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/deephaven/agent-skills --skill deephaven-core-query-writing命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | deephaven-core-query-writing |
| description | Work with Deephaven for real-time data processing. Use for table queries, joins, aggregations, time-series, UI dashboards, plotting (dx), Kafka streaming, Iceberg integration. Triggers on mentions of Deephaven, tables or queries, and related concepts. |
| license | Apache-2.0 |
| metadata | {"author":"Deephaven Data Labs","version":"0.1.0"} |
Read the relevant reference before writing code — Deephaven APIs differ from similar libraries.
| Reference | Read before code that... |
|---|---|
joins.md | uses natural_join, aj, raj, exact_join, range_join |
aggregations.md | uses agg_by, sum_by, avg_by, count_by, 20+ aggregators |
updateby.md | uses rolling/cumulative ops, EMAs, forward fill |
time-operations.md | parses/bins/manipulates timestamps; calendars, timezones |
kafka.md | consumes from / produces to Kafka |
iceberg.md | reads/writes Iceberg tables |
ui.md | builds dashboards, components, hooks, ui.table |
plotting.md | makes charts with dx |
csv.md | imports/exports CSVs, always read when importing CSV |
print() tables and never convert to pandas just to print.where().java.lang.Math, and the auto-imported query-language functions.__import__() or inline imports.from deephaven import (
empty_table,
new_table,
ring_table,
time_table,
)
from deephaven.column import double_col, string_col
# Empty table with formulas
t = empty_table(100).update(["X = i", "Y = X * 2"])
# New table from columns
t = new_table(
[string_col("Sym", ["AAPL", "GOOG"]), double_col("Price", [150.0, 140.0])]
)
# Ticking time table (real-time); accepts duration strings
t = time_table("PT1s")
# Ring table — bounded size, keeps last N rows
source = time_table("PT1s")
t = ring_table(source, capacity=1000)
print(table) only shows the object reference. To get actual cell values from a 1-row table, drop to the Java backing: table.j_table.getColumnSource("Col").get(table.j_table.getRowSet().firstRowKey()).
from deephaven import empty_table
t = empty_table(100).update(["A = i", "B = i * 2", "OldName = i", "Unwanted = i"])
# only named cols, results in RAM.
# Best for: subset with expensive formula or frequent access.
t.select(["A", "B", "C = A + B"])
# only named cols, recomputed on access (no RAM).
# Best for: subset with cheap formula, sparse access, or memory pressure.
t.view(["A", "B", "C = A + B"])
# all cols + new, results in RAM.
# Best for: results that feed downstream ops (joins, aggs, further updates).
t.update(["C = A + B", "D = sqrt(C)"])
# all cols + new, recomputed on access.
# Best for: display-only columns, or when memory is a concern.
t.update_view(["C = A + B"])
# all cols + new, memoized by input value.
# Best for: few distinct inputs relative to row count (e.g. category lookups).
t.lazy_update(["C = A + B"])
t.drop_columns(["Unwanted"])
t.select_distinct(["A"]) # unique; multi-col → unique tuples
t.rename_columns(["NewName = OldName"])
from deephaven import empty_table, new_table
from deephaven.column import string_col
t = empty_table(4).update(
[
"Sym = i % 2 == 0 ? `AAPL` : `GOOG`",
"Price = 50.0 + i * 50.0",
"Timestamp = parseInstant(`2024-06-01T00:00:00 UTC`) + i * 'P1d'",
]
)
filter_table = new_table([string_col("Sym", ["AAPL"])])
t.where("Price > 100")
t.where(["Sym = `AAPL`", "Price > 100"]) # list = AND
t.where("Sym.startsWith(`A`)") # Java String methods
t.where("Sym in `AAPL`, `GOOG`") # set membership (fast)
t.where_in(filter_table, "Sym") # / where_not_in
t.where("Timestamp > parseInstant(`2024-01-01T00:00:00 America/New_York`)")
sym = "AAPL" # dynamic — backtick the value
t.where(f"Sym = `{sym}`")
Read references/joins.md before using joins.
natural_join (add cols from right; NULL if no match), exact_join (errors unless exactly one match), join (all matching combinations).aj (closest ≤ timestamp), raj (closest ≥ timestamp).range_join — match within a range, aggregate results.from deephaven import merge; merge([t1, t2]) (column names and types must match).Read references/aggregations.md before using aggregations.
sum_by, avg_by, min_by, max_by, median_by, std_by, var_by, abs_sum_by — e.g. t.sum_by("Sym").count_by("Count", "Sym") — row count per group (first arg is output col name).first_by / last_by, head_by(N, ...) / tail_by(N, ...) — first/last row(s) per group.weighted_avg_by / weighted_sum_by — first arg is the weight col.agg_by([...], by=[...]) — multiple aggs in one pass (agg.avg(...), agg.sum_(...), …).group_by / ungroup — collect into arrays / explode back. partition_by — split into sub-tables.Dedicated aggs operate on ALL non-key columns and throw UnsupportedOperationException on String/Timestamp. Fix: .view() to keep only key + numeric first, or use agg_by with explicit cols.
Literals: Boolean true/false (lowercase). Int 42, Long 42L, Double 3.14 (no 1_000L underscores). Bare integers in / produce doubles — (year / 10) * 10 → 1987.0; fix with year - year % 10 or (int)(year / 10) * 10. String: backticks. DateTime: parseInstant(\2024-01-01T12:00:00 America/New_York`)— short aliases likeNYdo NOT work. Duration'PT1h30m'. Period 'P1y2m3d'`.
Built-ins: i row index (0-based); ii or k row key (stable).
from deephaven import empty_table
# Ternary + null handling
t = empty_table(10).update(
[
"Price = i * 25.0",
"Category = Price > 100 ? `High` : `Low`",
"Value = i % 2 == 0 ? NULL_INT : i",
"Safe = isNull(Value) ? 0 : Value",
]
)
from deephaven import empty_table, read_csv, write_csv
from deephaven.parquet import read, write
t = empty_table(10).update(["X = i", "Y = X * 2"])
write_csv(t, "/tmp/o.csv")
t_csv = read_csv("/tmp/o.csv")
write(t, "/tmp/o.parquet")
t_pq = read("/tmp/o.parquet")