PHPMem v2.0.1

Version
1.6.45
Uptime
17 days 4 hours 25 minutes 56 seconds

Memory

Total
512MB
Used
12,72MB (2.48%)
Free
499,28MB

Keys

Current
14 060
Total (since start)
40 994
Evictions
0
Reclaimed
760
Expired Unfetched
0
Evicted Unfetched
0

Connections

Current
15 / 1 024 max
Total
200 469
Rejected
0
llm:b67d59c6aac5839cf5075947d27fb5f1cd0d73a8acf443d974362219f1165300
TTL 5 days 22 hours 43 minutes 27 seconds Size 2,83KB Export
Edit
**Overall trend: deal activity is shrinking, while the typical deal is getting larger. Total dollars are too noisy to call a trend, and there is no reliable seasonality.** **1. Deal count: rose, peaked in 2015–16, then declined** - Reported deals per year (yearly query): 929 in 2015, 993 in 2016, 687 in 2017, 309 in 2018, 111 in 2019 and 7 in 2020. Only January 2020 appears in the data. - Monthly counts jumped from about 51–62 in early 2015 to 86–98 from mid-2015. They peaked at 104 in January 2016, then slid. - By late 2018 the count was in single or low double digits: 7 in October, 18 in November and 7 in December. Early 2019 had only 3 per month. - This is a decline of roughly 90% from the peak. - Part of this may be a coverage effect rather than a real collapse. Later years have far fewer rows, and the dataset looks like a compiled news-based list. I can't confirm that from the data, so treat the steepness of the drop with caution. **2. Deal size: trending up** - The median disclosed ticket by year was about $1.5M in 2015, $1.0M in 2016, $2.25M in 2017, $3.9M in 2018, $12M in 2019 and $9M in the January 2020 deals. - Fewer deals are being reported, but the ones that are reported are larger. The share of deals with a disclosed amount also rises, from about 69% in 2015 to about 94% in 2019, which suggests the data became more selective. **3. Total dollars: volatile and dominated by mega-deals** - Disclosed totals by year were about $8.6B in 2015, $10.4B in 2017, $5.1B in 2018 and $9.7B in 2019. The 2016 total came back as NaN, so it can't be compared. - Monthly totals swing widely, for example $2.26B in March 2017, $2.75B in August 2017 and $1.53B in July 2015, against typical months of $200–500M. A few very large deals drive these spikes, so the totals show no steady direction. - NaN also appears in July 2016, where some amount values didn't parse. Fixing that would likely complete the 2016 total. **4. Seasonality: weak or none** - The strongest stretch was July–September 2015, with 88–98 deals per month and high dollar totals. The same months in 2016 and 2017 did not repeat it. - January was high in 2016 (104 deals) but not in 2017 (72) or 2018 (37). - The year-over-year decline overwhelms any calendar pattern. With only a few years of data and no repeating peak, I wouldn't claim seasonality. **Data quality notes** - One row has the year "0015", a mistyped date for what is probably July 2015. - Seven rows have no date. - The monthly query matched 60 months but only the first 50 were returned, so I relied on the yearly results for 2019 onward. ```json {"charts":[{"kind":"combo","x":"month","series":["deals","median_usd"],"line_series":["median_usd"],"title":"Monthly deals and median ticket size"}]} ```